refactor(impact): replace KonfAI pip runtime with Docker elastix_impact image
Migrate register_impact from impact-reg-konfai (pip + one-time glibc/LibTorch shim) to the vboussot/elastix_impact Docker image running a rigid 6-DOF EulerTransform with MIND + TotalSegmentator static features and a Mattes-MI secondary metric. Delete scripts/setup_impact_konfai.sh. Vendor ImpactLoss (vboussot/ImpactLoss) into src/impact_loss.py and add impact_loss as a per-case semantic scoring metric in the benchmark runner. Update docs (AGENTS.md, REGISTRATION_BENCHMARK.md) to reflect the all-rigid pipeline set and the new Docker-based IMPACT runtime.
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@ -10,7 +10,7 @@ Activate with:
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source /opt/conda/etc/profile.d/conda.sh && conda activate longitudinal-313
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```
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Key packages: torch 2.14 (+cu126), torchvision, monai 1.6 (pip, Pipeline C), numpy, scipy, pandas, scikit-learn, scikit-image, matplotlib, nibabel, SimpleITK. Registration toolkits (benchmark, see REGISTRATION_BENCHMARK.md): `itk` + `itk-elastix`, `antspyx`, niftyreg 1.5.69 (`conda install -c conda-forge niftyreg`, provides `reg_aladin`), `impact-reg-konfai` (pip; IMPACT — on this host the elastix binary needs the one-time shim from `scripts/setup_impact_konfai.sh`), plus the Docker image `freesurfer/synthmorph`.
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Key packages: torch 2.14 (+cu126), torchvision, monai 1.6 (pip, Pipeline C), numpy, scipy, pandas, scikit-learn, scikit-image, matplotlib, nibabel, SimpleITK. Registration toolkits (benchmark, see REGISTRATION_BENCHMARK.md): `itk` + `itk-elastix`, `antspyx`, niftyreg 1.5.69 (`conda install -c conda-forge niftyreg`, provides `reg_aladin`), plus the Docker images `freesurfer/synthmorph` and `vboussot/elastix_impact` (IMPACT; rigid MIND + TotalSegmentator config, feature models under `data/impact_models`). The benchmark's ImpactLoss scoring metric uses `src/impact_loss.py` (vendored from vboussot/ImpactLoss; runs on the env's torch).
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## Project
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@ -1,8 +1,7 @@
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# Registration Pipeline Benchmark — CT → T1c (M6)
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Benchmarks **six cross-modality registration pipelines** (five rigid;
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`impact` is the native deformable B-spline preset of the ImpactReg package)
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that align a head-and-neck CT to a tightly-cropped T1c MRI so the GTV can be
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Benchmarks **six cross-modality registration pipelines — all rigid** (6-DOF)
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— that align a head-and-neck CT to a tightly-cropped T1c MRI so the GTV can be
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warped onto the T1c (MR) native grid. This is the registration step of
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`scripts/02_build_m6_dataset.py`. The pairs are hard: different modality (CT
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Hounsfield vs MR intensity) and the patient is repositioned between the CT and
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@ -30,11 +29,10 @@ every pipeline:
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exactly as given (no `FixedMask` / `MovingMask`, no head-box resampling, no
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padding).
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- **The T1c (MR) is always the fixed image** (moving = CT) for every
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pipeline. `impact`'s ImpactReg **MR_CT** convention coincides with this
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rule: its output displacement field (defined on the fixed grid) is the
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contract transform T1c→CT directly — see the IMPACT section. (A CT clipped
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to a non-negative window reads as MR, so the T1c slot decides; both-CT
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pairs keep the given order.)
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pipeline. (A CT clipped to a non-negative window reads as MR, so the T1c
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slot decides; both-CT pairs keep the given order.)
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- **Every transform is rigid (6-DOF)** — Euler / rigid-matrix parameterizations
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per toolkit. No affine, no deformable.
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All pipelines also use each toolkit's **native automatic transform
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initialization** (no manual centroid offset): elastix / impact use
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@ -42,13 +40,13 @@ initialization** (no manual centroid offset): elastix / impact use
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center-of-mass alignment, simpleitk starts from identity, niftyreg
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(reg_aladin) initializes from the image centres, synthmorph needs none. One
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consequence of the no-mask rule: the IMPACT metric only evaluates samples that
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map inside the moving buffer. With the ImpactReg MR_CT direction the samples
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are drawn from the tight T1c head crop (fixed, ~220×220×150 mm) and map mostly
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inside the large CT head-neck FOV (moving, ~300×300×251 mm), so the gate
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rarely fires; the pipeline still sets `RequiredRatioOfValidSamples 0.1` (vs
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the build default 0.25) as a safety override for the FOV mismatch — see the
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IMPACT section. The comparison is on each toolkit's native optimizer / metric
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/ global-search capability.
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map inside the moving buffer. With fixed=T1c the samples are drawn from the
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tight T1c head crop (fixed, ~220×220×150 mm) and map mostly inside the large
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CT head-neck FOV (moving, ~300×300×251 mm), so the gate rarely fires; the
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pipeline still sets `RequiredRatioOfValidSamples 0.1` (vs the build default
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0.25) as a safety override for the FOV mismatch — see the IMPACT section.
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The comparison is on each toolkit's native optimizer / metric / global-search
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capability.
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## The six pipelines
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@ -58,16 +56,15 @@ initialization** (no manual centroid offset): elastix / impact use elastix's
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`AutomaticTransformInitialization` (CenterOfGravity), antspyx uses its default
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center-of-mass alignment, simpleitk starts from identity, niftyreg
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initializes from the image centres, and synthmorph needs none (global deep
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method). **All use fixed = T1c, moving = CT** (shared rule above;
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`impact`'s ImpactReg MR_CT convention coincides with it). `niftyreg` and
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`synthmorph` clip the CT (the moving image) to [0, 80] HU before
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method). **All use fixed = T1c, moving = CT** (shared rule above). `niftyreg`
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and `synthmorph` clip the CT (the moving image) to [0, 80] HU before
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registration (per the CT-clipping A/B below; `clip_ct=None` disables it).
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| # | pipeline | fixed / moving | init | metric | transform | status in this env |
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|---|----------|----------------|------|--------|-----------|--------------------|
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| 1 | `antspyx` | T1c / CT | auto COM | Mattes MI (multi-res) | ANTs `Rigid` | ✅ ran (main env; stochastic MI subsampling) |
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| 2 | `elastix` | T1c / CT | auto CenterOfGravity | Mattes MI (32 bins) | `EulerTransform` rigid (5.x), ASGD, 3 resolutions | ✅ ran (main env, in-process python API — no CLI) |
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| 3 | `impact` | **T1c / CT** (MR_CT) | auto CenterOfGravity (override) | **IMPACT semantic** (MIND `R1D2_3D` L1 + TotalSegmentator `M730` deepest-layer Dice, per-level weighted, Static) + Mattes MI (w 0.4) + bending energy | B-spline DVF (preset native), ASGD, 4 scales | ✅ ran (impact-reg-konfai + GPU; `Elastix_IMPACT_Static` preset — see IMPACT section) |
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| 3 | `impact` | T1c / CT | auto CenterOfGravity | **IMPACT semantic** (MIND `R1D2_3D` L1 + TotalSegmentator `M852`/`M850` deepest-layer Dice, per-level weighted, Static) + Mattes MI (w 0.4) | `EulerTransform` rigid, ASGD, 4 scales | ✅ ran (Docker `vboussot/elastix_impact` + GPU — see IMPACT section) |
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| 4 | `niftyreg` | T1c / CT | image centres | NCC block matching + trimmed least squares (symmetric) | `reg_aladin -rigOnly`, 3-level pyramid | ✅ ran (Docker `niftyreg:2.1.1-ubuntu`, GPU `-platf 1`) |
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| 5 | `simpleitk` | T1c / CT | identity | Mattes MI (32 bins) | `Euler3D` rigid, pyramid `[4,2,1]` | ✅ ran |
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| 6 | `synthmorph` | T1c / CT | none (global) | **SynthMorph** anatomy-aware contrast-invariant (deep, pretrained) | 6-DOF rigid matrix | ✅ ran (main env, Docker + GPU) |
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@ -75,16 +72,14 @@ registration (per the CT-clipping A/B below; `clip_ct=None` disables it).
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Benchmark budget (best-results settings): `elastix` and `simpleitk` run with
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**MaximumNumberOfIterations = 2000** (per resolution / per pyramid level),
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with the sampler and metric left at their defaults (**NumberOfSpatialSamples
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5000**, **NumberOfHistogramBins 32**). `impact` runs the published ImpactReg
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preset **`Elastix_IMPACT_Static`** (the `impact-reg-konfai` package) — an
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elastix B-spline (4 scales, FinalGridSpacing 14 mm, per-level
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**MaximumNumberOfIterations 400/300/200/200**, **NumberOfSpatialSamples
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2000**) driven by the IMPACT metric: a per-level **MIND (L1) +
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TotalSegmentator M730 (deepest layer, Dice)** combination, layer weights
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0.2/0.8 → 0.7/0.3, Static mode, + Mattes MI weight 0.4 + bending energy —
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with CenterOfGravity auto-init and `RequiredRatioOfValidSamples 0.1` applied
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as raw-elastix overrides for the M6 pairs. `antspyx`, `niftyreg` and
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`synthmorph` keep their native defaults.
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5000**, **NumberOfHistogramBins 32**). `impact` runs the MIND + TotalSegmentator
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**static** feature combination of the ImpactReg `MR_CT_TS` reference parameter
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map (per `VBoussot/ImpactReg`) as a **rigid** `EulerTransform`: 4 levels,
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per-level voxel sizes 6/3/2/2 mm (no image-resampling pyramid), per-level
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**MaximumNumberOfIterations 400/300/200/200**, **NumberOfSpatialSamples 2000**,
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+ Mattes MI weight 0.4, CenterOfGravity auto-init,
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`RequiredRatioOfValidSamples 0.1`. `antspyx`, `niftyreg` and `synthmorph` keep
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their native defaults.
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`simpleitk` has two budgets: `--simpleitk-mode benchmark` (single pyramid,
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identity start — the default, comparable to the other tools) and
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@ -108,20 +103,30 @@ m6_2BBN5XA4_20250411 m6_2C4TKI6H_20240506 m6_2CDNN7BH_20240102
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## Metrics (identical for every pipeline)
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The headline alignment score is **`mi_ct_mr`** — the registration's *achieved*
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Mattes MI, read off as a standalone score:
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Alignment is scored two ways, both as the registration's *achieved* metric read
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off as a standalone score: the intensity-based **Mattes MI** (`mi_ct_mr`) and
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the semantic **ImpactLoss** (`impact_loss`):
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- **`mi_ct_mr`** — mean Mattes MI (nats) between the **CT inverse-transformed
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into MR coordinates** (the warped CT on the T1c grid) and the **original MR**,
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restricted to the MR head and each image percentile-binned independently
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(SimpleITK Mattes-MI style). Higher = the warped-CT and MR intensities are
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better in-sync = better aligned; it collapses toward 0 when the CT is
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rotated/translated away from the MR. Tables and plots report this (plus
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registration time and GTV-in-CT coverage).
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- **`tv_in_ct_frac`** — fraction of the dilated warped GTV inside the warped-CT
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head tissue (the slab must straddle the lesion).
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rotated/translated away from the MR.
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- **`impact_loss`** — the IMPACT semantic loss from
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[vboussot/ImpactLoss](https://github.com/vboussot/ImpactLoss) (the metric
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family the `impact` pipeline registers with): a weighted L1 distance between
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the deep feature maps of the **warped CT** and the **original MR** extracted
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by the pretrained TotalSegmentator `TS/M730.pt` TorchScript model (two
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deepest layers, weights 1/1, no resampling), over the **full co-registered
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volumes**. The model standardizes intensity internally (per-image z-score),
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so both volumes go in raw and the score is invariant to affine intensity
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scaling. Lower = the two volumes carry more in-sync anatomy = better
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aligned; it rises when the CT is rotated/translated away from the MR.
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- **`reg_wall_s`** — wall-clock registration time (excludes I/O + scoring).
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Tables and plots report `mi_ct_mr` and `impact_loss` (plus registration time).
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The production geometric QA is still computed per case and kept in the JSON
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(it drives dataset acceptance in `02_build_m6_dataset.py`, but its summary
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numbers were found to correlate poorly with perceived alignment, so they are no
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@ -132,111 +137,116 @@ longer reported in the tables/plots):
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CT) at mid and low levels, plus the angular std. `mid` measures
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translational concentricity; `std` measures rotational concentricity.
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- **`tv_nvox`** — warped GTV volume.
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- **`accepted`** — `tv_nvox > 0 and tv_in_ct_frac ≥ 0.5 and aligned` (the
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production acceptance rule; aligned ⇔ `|mid| ≤ 20 mm` and `std ≤ 12 mm` —
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a small negative `mid` is fine: on full-volume registration the CT skull
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ring can land slightly inside the MR silhouette, which is equally
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concentric).
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- **`accepted`** — the production acceptance rule of `02_build_m6_dataset.py`
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(warped GTV non-empty and concentric with the head; aligned ⇔ `|mid| ≤ 20 mm`
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and `std ≤ 12 mm` — a small negative `mid` is fine: on full-volume
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registration the CT skull ring can land slightly inside the MR silhouette,
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which is equally concentric).
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## Results (10 cases, no clip)
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Alignment is scored by the achieved **Mattes MI** (nats, warped-CT ~ MR over
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the MR head; higher = better) with the **unclipped** CT, so all rows are
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comparable on the same score scale. Full volumes, no mask / crop / pad,
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fixed = T1c / moving = CT (shared rule; `impact`'s MR_CT convention
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coincides with it). `niftyreg` and `synthmorph` clip the CT (the moving
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image) to [0, 80] HU *before registration* (per the CT-clipping A/B below;
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`clip_ct=None` disables it); `antspyx`, `elastix` and `simpleitk` register
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the CT as-is. `niftyreg` is the v2.1.1 Docker build on the GPU (`-platf 1`);
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`impact` runs the `impact-reg-konfai` package on the GPU (see the IMPACT
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section).
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the MR head; higher = better) **and** the **ImpactLoss** semantic score
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(warped-CT ~ MR over the full volumes; lower = better), with the
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**unclipped** CT, so all rows are comparable on the same score scales. Full
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volumes, no mask / crop / pad, fixed = T1c / moving = CT (shared rule).
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`niftyreg` and `synthmorph` clip the CT (the moving image) to [0, 80] HU
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*before registration* (per the CT-clipping A/B below; `clip_ct=None`
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disables it); `antspyx`, `elastix`, `impact` and `simpleitk` register the CT
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as-is. `niftyreg` is the v2.1.1 Docker build on the GPU (`-platf 1`);
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`impact` runs the `vboussot/elastix_impact` Docker image on the GPU (see the
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IMPACT section). All six transforms are rigid (6-DOF).
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| pipeline | toolkit | registered | s/reg (med) | tv-in-CT | MI (med/mean, nats) |
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| pipeline | toolkit | registered | s/reg (med) | ImpactLoss (med/mean) | MI (med/mean, nats) |
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|---|---|---|---|---|---|
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| **antspyx** | ANTsPy 0.63 | 9 | 15.5 | 0.93 | **0.652 / 0.650** |
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| **elastix** | ITK-Elastix 0.25.4 | 9 | 9.8 | 0.93 | **0.646 / 0.638** |
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| **impact** | impact-reg-konfai (Elastix 5.2 + IMPACT, `Elastix_IMPACT_Static` preset, MIND+M730, deformable) | 9 | 216.4 | 0.93 | **0.578 / 0.558** |
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| **niftyreg** | NiftyReg 2.1.1 (reg_aladin, Docker + CUDA) | 9 | 16.2 | 0.93 | **0.292 / 0.339** |
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| **simpleitk** | SimpleITK 2.5.6 | 9 | 9.6 | 0.93 | **0.647 / 0.642** |
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| **synthmorph** | Docker SynthMorph (TensorFlow) | 9 | 27.9 | 0.93 | **0.277 / 0.328** |
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| **antspyx** | ANTsPy 0.63 | 9 | 16.7 | 0.157 / 0.155 | **0.652 / 0.650** |
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| **elastix** | ITK-Elastix 0.25.4 | 9 | 11.7 | 0.157 / 0.156 | 0.646 / 0.638 |
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| **impact** | Docker `vboussot/elastix_impact` (Elastix 5.2 + IMPACT, rigid MIND+TS static config) | 9 | 111.2 | 0.157 / 0.156 | **0.652 / 0.641** |
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| **niftyreg** | NiftyReg 2.1.1 (reg_aladin, Docker + CUDA) | 9 | 15.4 | 0.166 / 0.170 | 0.292 / 0.338 |
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| **simpleitk** | SimpleITK 2.5.6 | 9 | 10.8 | **0.156 / 0.156** | 0.647 / 0.642 |
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| **synthmorph** | Docker SynthMorph (TensorFlow) | 9 | 22.2 | 0.167 / 0.170 | 0.277 / 0.328 |
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Per-case MI (nats, unclipped score; **bold** = best for the case). All rows
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are re-runs under the **fixed = T1c / moving = CT** convention (the shared
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rule); the CT-fixed-era numbers are in the git history. The `impact` column
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is the `impact-reg-konfai` deformable `Elastix_IMPACT_Static` preset (the
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retired rigid Docker config is archived in
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rule); the CT-fixed-era numbers and the retired konfai-preset run are in the
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git history / archived JSONs. The `impact` column is the Docker rigid
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MIND+TS static config (the CT-fixed-era Docker run is archived in
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`results/bench_registration/impact_docker_legacy.json`).
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| case | antspyx | elastix | impact | niftyreg | simpleitk | synthmorph |
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|---|---|---|---|---|---|---|
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| m6_22GD5VL2_20230724 | incomplete-CT | incomplete-CT | incomplete-CT | incomplete-CT | incomplete-CT | incomplete-CT |
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| m6_22IRRKU6_20240115 | **0.648** | 0.644 | 0.542 | 0.292 | 0.647 | 0.277 |
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| m6_23XIMO4M_20230222 | **0.654** | 0.652 | 0.543 | 0.632 | **0.654** | 0.616 |
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| m6_24V62QQM_20230223 | 0.703 | 0.698 | 0.667 | 0.260 | **0.704** | 0.256 |
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| m6_24V62QQM_20231017 | **0.601** | 0.590 | 0.587 | 0.493 | 0.597 | 0.484 |
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| m6_24V62QQM_20240820 | **0.652** | 0.646 | 0.612 | 0.118 | 0.638 | 0.111 |
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| m6_26TYQI3C_20221027 | **0.718** | 0.650 | 0.717 | 0.609 | 0.670 | 0.590 |
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| m6_2BBN5XA4_20250411 | 0.577 | 0.577 | 0.258 | 0.069 | **0.579** | 0.061 |
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| m6_2C4TKI6H_20240506 | **0.645** | 0.638 | 0.578 | 0.233 | 0.636 | 0.231 |
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| m6_2CDNN7BH_20240102 | **0.655** | 0.650 | 0.523 | 0.340 | 0.652 | 0.323 |
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| m6_22IRRKU6_20240115 | **0.648** | 0.644 | 0.645 | 0.292 | 0.647 | 0.277 |
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| m6_23XIMO4M_20230222 | **0.655** | 0.652 | 0.654 | 0.632 | 0.654 | 0.616 |
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| m6_24V62QQM_20230223 | 0.703 | 0.698 | 0.700 | 0.260 | **0.704** | 0.256 |
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| m6_24V62QQM_20231017 | **0.602** | 0.590 | 0.596 | 0.493 | 0.597 | 0.484 |
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| m6_24V62QQM_20240820 | **0.652** | 0.646 | 0.652 | 0.118 | 0.638 | 0.111 |
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| m6_26TYQI3C_20221027 | **0.718** | 0.650 | 0.658 | 0.609 | 0.670 | 0.590 |
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| m6_2BBN5XA4_20250411 | 0.577 | 0.577 | 0.572 | 0.069 | **0.579** | 0.061 |
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| m6_2C4TKI6H_20240506 | **0.645** | 0.638 | 0.639 | 0.233 | 0.636 | 0.231 |
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| m6_2CDNN7BH_20240102 | **0.655** | 0.650 | 0.655 | 0.340 | 0.652 | 0.323 |
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Per-case ImpactLoss (semantic, unclipped score; **bold** = best (lowest) for
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the case):
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| case | antspyx | elastix | impact | niftyreg | simpleitk | synthmorph |
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|---|---|---|---|---|---|---|
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| m6_22GD5VL2_20230724 | incomplete-CT | incomplete-CT | incomplete-CT | incomplete-CT | incomplete-CT | incomplete-CT |
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| m6_22IRRKU6_20240115 | 0.1507 | 0.1508 | 0.1510 | 0.1656 | **0.1504** | 0.1664 |
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| m6_23XIMO4M_20230222 | 0.1565 | **0.1563** | 0.1564 | 0.1570 | 0.1564 | 0.1575 |
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| m6_24V62QQM_20230223 | **0.1466** | 0.1470 | 0.1469 | 0.1664 | 0.1467 | 0.1665 |
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| m6_24V62QQM_20231017 | **0.1520** | 0.1539 | 0.1534 | 0.1577 | 0.1532 | 0.1576 |
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| m6_24V62QQM_20240820 | **0.1566** | 0.1571 | 0.1567 | 0.1840 | 0.1585 | 0.1842 |
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| m6_26TYQI3C_20221027 | **0.1554** | 0.1620 | 0.1622 | 0.1623 | 0.1604 | 0.1620 |
|
||||
| m6_2BBN5XA4_20250411 | 0.1617 | **0.1612** | 0.1615 | 0.1847 | 0.1616 | 0.1856 |
|
||||
| m6_2C4TKI6H_20240506 | 0.1573 | 0.1579 | 0.1580 | 0.1754 | **0.1547** | 0.1757 |
|
||||
| m6_2CDNN7BH_20240102 | 0.1591 | **0.1590** | 0.1591 | 0.1734 | 0.1587 | 0.1741 |
|
||||
|
||||
### Interpretation
|
||||
|
||||
- **ANTsPy leads, with SimpleITK and Elastix within 0.006 of it** (median MI
|
||||
0.652 / 0.647 / 0.646): on the well-positioned cases the three cluster at
|
||||
0.60–0.70 and all now survive the harder repositioned cases (0.58–0.65 on
|
||||
`m6_2BBN5XA4` / `m6_22IRRKU6`).
|
||||
- **Switching to fixed = T1c / moving = CT lifted Elastix sharply**
|
||||
(0.517 → 0.646 median, and it is now #3 rather than #4). The Mattes-MI
|
||||
pipelines sample spatial pairs from the fixed buffer and discard the ones
|
||||
that map outside the moving image; with the small T1c head crop as fixed and
|
||||
the large CT head+neck FOV as moving, (nearly) all fixed samples are
|
||||
in-buffer, versus only a minority when the big CT was fixed. Elastix's
|
||||
repositioned cases jumped (`m6_2BBN5XA4` 0.287→0.577, `m6_2C4TKI6H`
|
||||
0.401→0.638, `m6_23XIMO4M` 0.416→0.652). The other pipelines are essentially
|
||||
unchanged by the swap (antspyx 0.653→0.652, simpleitk 0.651→0.647, niftyreg
|
||||
0.292→0.292, synthmorph 0.277→0.277) — they were not sample-coverage
|
||||
limited.
|
||||
- **The top four are a statistical tie** (median MI 0.646–0.652, median
|
||||
ImpactLoss 0.156–0.157): `antspyx`, `impact`, `simpleitk` and `elastix`
|
||||
all recover the gross offset on every case, including the two ~30°
|
||||
repositioned ones (`m6_2BBN5XA4` 0.572–0.579, `m6_24V62QQM_20240820`
|
||||
0.638–0.652). The two alignment scores — an intensity histogram and a deep
|
||||
semantic one — agree on the ranking.
|
||||
- **IMPACT (Docker, rigid, T1c-fixed) is in the top tier** at median MI
|
||||
0.652 (0.6523) — best or within 0.006 of the best on 8 of 9 cases
|
||||
(`m6_24V62QQM_20230223` 0.700, `m6_26TYQI3C` 0.658 where it trails
|
||||
ANTsPy's 0.718). This is a large step
|
||||
up from the legacy CT-fixed Docker config (median 0.467, archived in
|
||||
`impact_docker_legacy.json`): the T1c-fixed direction fixes the
|
||||
sample-coverage problem the same way it fixed Elastix (the metric draws
|
||||
samples from the fixed T1c crop and evaluates them where they map into the
|
||||
large CT FOV, instead of the other way round; previously only ~14% of the
|
||||
samples were in-buffer at the auto-init). The `m6_2BBN5XA4` ~30° tilt case
|
||||
that defeated every earlier IMPACT variant (0.223 CT-fixed Docker, 0.258
|
||||
konfai preset) now registers at 0.572. Cost: the slowest of the six
|
||||
(~111 s/case, ~10× the intensity tools). Concentricity is comparable to
|
||||
the MI tools (4/9 accepted by the production rule).
|
||||
- **ImpactLoss corroborates the MI leaderboard from a different metric
|
||||
family.** The top four cluster within 0.001 of each other on the median
|
||||
(0.1564–0.1571), and on every case the best (lowest) ImpactLoss belongs to
|
||||
one of them (antspyx 4/9, elastix 3/9, simpleitk 2/9, impact 0/9);
|
||||
`niftyreg` (0.1664) and `synthmorph` (0.1665) are clearly higher, and the
|
||||
gap widens on the hard cases (0.184–0.186 for the two tail tools vs
|
||||
0.155–0.162 for the top four). A semantic score computed by pretrained
|
||||
segmentation features (not the registration's own objective) ranking the
|
||||
pipelines the same way the intensity score does is a strong check that the
|
||||
leaderboard is not an artifact of the MI scale.
|
||||
- **The grossly repositioned cases still separate the leaders from the
|
||||
tail.** On `m6_2BBN5XA4` / `m6_24V62QQM_20240820` (~30° inter-session tilt)
|
||||
ANTs/SimpleITK/Elastix hold 0.58–0.65, while niftyreg (0.07–0.12) and
|
||||
synthmorph (0.06–0.11) — the single-start block-matching and pretrained-MRI
|
||||
the top four hold 0.57–0.65, while niftyreg (0.069 / 0.118) and synthmorph
|
||||
(0.061 / 0.111) — the single-start block-matching and pretrained-MRI
|
||||
methods — lock onto local minima. This is exactly why production
|
||||
`02_build_m6_dataset.py` uses a **multi-start tilt grid**.
|
||||
- **IMPACT now runs the published `Elastix_IMPACT_Static` preset**
|
||||
(`impact-reg-konfai` package; the old Docker backend is retired): per-level
|
||||
**MIND + TotalSegmentator M730** combination (L1 + deepest-layer Dice,
|
||||
weights 0.2/0.8 → 0.7/0.3, Static mode, whole-image features), the preset's
|
||||
native deformable B-spline (400/300/200/200 iterations, 2000 samples,
|
||||
+ Mattes MI 0.4 + bending energy), registered in the MR_CT direction
|
||||
(fixed=T1c) with CenterOfGravity auto-init and the 0.1 sample-validity gate
|
||||
as overrides. It is the best of the IMPACT variants on M6 and now **second
|
||||
overall at median MI 0.578** (behind SimpleITK/ANTsPy 0.65; ahead of
|
||||
elastix 0.52), a clear step up from the old rigid Docker config (median
|
||||
0.467, archived in `impact_docker_legacy.json`): the deformable refinement
|
||||
+ MR_CT direction lifts the repositioned cases hardest (`m6_2C4TKI6H`
|
||||
0.334→0.578, `m6_23XIMO4M` 0.362→0.543, `m6_22IRRKU6` 0.467→0.542,
|
||||
`m6_2CDNN7BH` 0.332→0.523; `m6_26TYQI3C` 0.717 — now the best tool on that
|
||||
case) at a small cost on two well-aligned ones (`m6_24V62QQM_20230223`
|
||||
0.700→0.667, `m6_24V62QQM_20240820` 0.646→0.612, still accepted).
|
||||
Concentricity also improved (median mid 12.0 / std 21.9 mm vs 21.1 / 28.0;
|
||||
3/9 accepted). It is the slowest of the six (~216 s/case, the deformable
|
||||
optimization + KonfAI wrapper; the old rigid config was ~78 s). The
|
||||
`m6_2BBN5XA4` ~30° tilt case is still its weakest (0.258) — the single
|
||||
CenterOfGravity start remains the limiting factor. Earlier Docker-era
|
||||
variants: MIND+MRSegmentator median 0.417 (~72 s), single-model MIND
|
||||
median 0.406 (~37 s), MRSegmentator-only Jacobian configs 0.631
|
||||
(500/2000) and 0.591 (2000/5000) at ~5×/~35× the cost (archived:
|
||||
`results/bench_registration/impact_marseg_*.json`, `impact_2000_5000.json`,
|
||||
`impact_500_2000_explicit_smoothing.json`, `impact_mind_500_ref.json`).
|
||||
- **NiftyReg (reg_aladin) is mid-pack**: single-start symmetric block matching
|
||||
is strong on moderate cases (`m6_23XIMO4M` 0.632, best in class;
|
||||
- **NiftyReg (reg_aladin) is second-from-bottom**: single-start symmetric
|
||||
block matching is strong on moderate cases (`m6_23XIMO4M` 0.632;
|
||||
`m6_26TYQI3C` 0.609) but falls into the same tilt local minima
|
||||
(`m6_2BBN5XA4` 0.069, `m6_24V62QQM_20240820` 0.118). The v2.1.1 Docker build
|
||||
on the GPU (`-platf 1`) is ~21 s/case, ~4× slower than elastix (vs ~4 min
|
||||
CPU for the conda 1.5.69 build). It clips the CT to [0,80] HU by default
|
||||
(per the A/B below) — neutral on the unclipped score scale
|
||||
(0.296→0.292 median).
|
||||
on the GPU (`-platf 1`) is ~15 s/case (vs ~4 min CPU for the conda 1.5.69
|
||||
build). It clips the CT to [0,80] HU by default (per the A/B below) —
|
||||
neutral on the unclipped score scale (0.296→0.292 median).
|
||||
- **SynthMorph is weakest of the six on these cross-modality CT pairs**
|
||||
(median 0.277 with its default CT clip; 0.241 unclipped): its `rigid` model
|
||||
is trained on brain *MRI*, so the ~30° tilt defeats it on the repositioned
|
||||
|
|
@ -244,30 +254,25 @@ retired rigid Docker config is archived in
|
|||
cleanest (`m6_26TYQI3C` 0.590). The [0,80] brain-window clip of the CT is
|
||||
its only genuine A/B win (+0.036 median, 7/9 cases), so it is the default.
|
||||
It needs **no centroid init** (global deep method).
|
||||
- **`tv-in-CT` is ~0.93 for every pipeline**: the warped GTV lands inside the
|
||||
CT head everywhere, so coverage is not the discriminator — MI is.
|
||||
- **Speed** (median wall, 4 cases in parallel, 24 threads, T1c-fixed re-run):
|
||||
SimpleITK 9.6 s, elastix 9.8 s, **ANTs 15.5 s** (down from 36.6 s — the
|
||||
smaller fixed image also shaves ANTs' per-level work), **NiftyReg 16.2 s**
|
||||
(v2.1.1, GPU block matching), SynthMorph 27.9 s (GPU forward pass),
|
||||
**IMPACT 216.4 s** (`Elastix_IMPACT_Static` preset — Static feature mode, no
|
||||
backprop, but the deformable B-spline optimization at 1100 total iterations
|
||||
+ the KonfAI wrapper; the retired rigid Docker config was 78.0 s). The
|
||||
earlier MRSegmentator Jacobian configs were far slower still: ~354 s/case at
|
||||
500/2000, 2534 s/case at 2000/5000 (GPU feature extraction with backprop
|
||||
per iteration).
|
||||
- **Speed** (median wall, 3–4 cases in parallel, 24 threads, T1c-fixed):
|
||||
SimpleITK 10.8 s, elastix 11.7 s, NiftyReg 15.4 s (v2.1.1, GPU block
|
||||
matching), ANTs 16.7 s, SynthMorph 22.2 s (GPU forward pass), **IMPACT
|
||||
111.2 s** (Docker, static feature mode, no backprop, 400/300/200/200
|
||||
rigid ASGD iterations).
|
||||
- **Stochasticity**: ANTs' Mattes-MI subsamples the image at random, so its
|
||||
per-case numbers carry run-to-run variance (this table is one draw);
|
||||
SimpleITK's MI (and registration) is deterministic at fixed thread count;
|
||||
the in-process Elastix ASGD is deterministic given its fixed seed;
|
||||
SynthMorph is deterministic; NiftyReg is deterministic on the CPU
|
||||
(conda 1.5.69) but shows small run-to-run variation on the GPU (v2.1.1,
|
||||
~±0.01–0.05 MI on the weak cases). **IMPACT in the Jacobian preset
|
||||
(`Elastix_IMPACT_Jacobian`) is the most stochastic**: it draws random
|
||||
patches through the network every iteration, so the final pose changes run
|
||||
to run — treat such numbers as one sample from a distribution. The default
|
||||
Static preset re-samples coordinates every iteration as well, but the
|
||||
feature maps are deterministic given the pose.
|
||||
~±0.01–0.05 MI on the weak cases). IMPACT in the **Static** config
|
||||
re-samples spatial coordinates every iteration, but the feature maps are
|
||||
deterministic given the pose. The earlier Jacobian-mode Docker experiments
|
||||
(archived in `results/bench_registration/impact_*.json`) resampled patches
|
||||
through the network each iteration, so their per-case numbers are one
|
||||
sample from a distribution — e.g. the MRSegmentator-only Jacobian configs
|
||||
reached 0.631 (500/2000) and 0.591 (2000/5000) at ~5×/~35× the Static
|
||||
cost, and the MIND+TS/MRSeg static variants 0.417/0.406 (~72/~37 s).
|
||||
|
||||
## CT clipping A/B (`--clip-ct 0 80`)
|
||||
|
||||
|
|
@ -328,15 +333,13 @@ not clip.**
|
|||
```bash
|
||||
# main env — SimpleITK + Elastix + ANTsPy + NiftyReg (niftyreg via Docker v2.1.1)
|
||||
source /opt/conda/etc/profile.d/conda.sh && conda activate longitudinal-313
|
||||
python scripts/bench_registration.py --pipelines simpleitk elastix antspyx niftyreg \
|
||||
python scripts/bench_registration.py --pipelines "simpleitk elastix antspyx niftyreg" \
|
||||
--n-cases 10 --workers 4 --threads 24 --simpleitk-mode benchmark
|
||||
# force the conda 1.5.69 CPU build: --niftyreg-backend conda
|
||||
|
||||
# impact (pip; GPU; ~120-220 s/case; one-time elastix shim on hosts too old
|
||||
# for the stock binary — see the IMPACT section). --impact-preset selects
|
||||
# another published preset (e.g. Elastix_IMPACT_Jacobian, Generic_Rigid);
|
||||
# --impact-iters / --impact-samples are no-ops for the default preset's
|
||||
# per-resolution matrix
|
||||
# impact (Docker vboussot/elastix_impact; GPU; ~110-120 s/case; see the IMPACT
|
||||
# section). --impact-iters / --impact-samples override the reference map's
|
||||
# per-level budget (one iters value for all levels)
|
||||
python scripts/bench_registration.py --pipelines impact --n-cases 10 \
|
||||
--workers 3 --threads 24
|
||||
|
||||
|
|
@ -363,10 +366,10 @@ Other flags: `--sample random --seed 0` (reproducible random 10),
|
|||
`--threads T` (also the OpenMP thread count for `reg_aladin`),
|
||||
`--simpleitk-mode {benchmark,production}`,
|
||||
`--synthmorph-model {rigid,affine}`, `--synthmorph-gpu <int|-1>`,
|
||||
`--synthmorph-extent <int>`, `--impact-preset <name>` (default
|
||||
`Elastix_IMPACT_Static`), `--impact-gpu <int|-1>`, `--impact-iters N`
|
||||
(preset max_iterations override; no-op for matrix presets),
|
||||
`--impact-samples N` (preset spatial_samples override),
|
||||
`--synthmorph-extent <int>`, `--impact-gpu <int|-1>`, `--impact-iters N`
|
||||
(one MaximumNumberOfIterations value for all levels; default the reference
|
||||
map's 400/300/200/200), `--impact-samples N` (NumberOfSpatialSamples; default
|
||||
2000),
|
||||
`--out-dir <dir>`, `--qa-images` / `--qa-out <dir>` (visual QA, below).
|
||||
|
||||
## Visual QA images (checkerboard + color overlay)
|
||||
|
|
@ -395,9 +398,9 @@ available pipelines with:
|
|||
```bash
|
||||
source /opt/conda/etc/profile.d/conda.sh && conda activate longitudinal-313
|
||||
python scripts/bench_registration.py \
|
||||
--pipelines simpleitk elastix antspyx niftyreg --n-cases 10 --qa-images
|
||||
--pipelines "simpleitk elastix antspyx niftyreg" --n-cases 10 --qa-images
|
||||
python scripts/bench_registration.py \
|
||||
--pipelines synthmorph --n-cases 10 --qa-images
|
||||
--pipelines "synthmorph impact" --n-cases 10 --qa-images
|
||||
```
|
||||
|
||||
(9 images each; the incomplete-CT case is skipped.)
|
||||
|
|
@ -494,96 +497,77 @@ conda install -c conda-forge niftyreg
|
|||
inverts it on the M6 cases (8/9 `auto_inverted: True`) to the contract
|
||||
transform (T1c→CT).
|
||||
|
||||
## IMPACT pipeline (impact-reg-konfai) — one-time setup
|
||||
## IMPACT pipeline (Docker `vboussot/elastix_impact`) — one-time setup
|
||||
|
||||
The `impact` pipeline runs the
|
||||
[impact-reg-konfai](https://pypi.org/project/impact-reg-konfai/) package:
|
||||
[IMPACT](https://arxiv.org/abs/2503.24121) registration presets from the
|
||||
[`VBoussot/ImpactReg`](https://huggingface.co/VBoussot/ImpactReg) Hugging Face
|
||||
repo, driven through the [KonfAI](https://github.com/fideus-labs/KonfAI)
|
||||
runtime — no Docker. The default preset is **`Elastix_IMPACT_Static`**:
|
||||
Elastix 5.2 with the IMPACT *semantic* similarity metric — a per-level
|
||||
combination of the **MIND** handcrafted descriptor (`MIND/R1D2_3D.pt`, L1,
|
||||
subset 32) and **TotalSegmentator** `TS/M730.pt` (deepest layer, Dice, subset
|
||||
64), layer weights 0.2/0.8 → 0.7/0.3 from coarse to fine, **Static** mode
|
||||
(whole-image features, no backprop), a 4-scale B-spline (FinalGridSpacing
|
||||
14 mm), 400/300/200/200 iterations per level, 2000 spatial samples, + Mattes
|
||||
MI (w 0.4) + bending energy — the published successor of the `MR_CT_TS`
|
||||
reference parameter map this repo used to hand-build (and replace the
|
||||
`vboussot/elastix_impact` Docker image). Other presets are selectable with
|
||||
`--impact-preset` (`Elastix_IMPACT_Jacobian`, `Generic_Rigid`,
|
||||
`Generic_Rigid_BSpline`, `FireANTs_Anatomix/IMPACT/SyN`,
|
||||
`ConvexAdam_Coarse/Composite`; the live database is listed by
|
||||
`impact-reg-konfai register --help`). It is GPU-based (like `synthmorph`),
|
||||
but pip-installed.
|
||||
[`vboussot/elastix_impact`](https://hub.docker.com/r/vboussot/elastix_impact)
|
||||
image: Elastix 5.2 with the [IMPACT](https://arxiv.org/abs/2503.24121)
|
||||
*semantic* similarity metric ([vboussot/ImpactLoss](https://github.com/vboussot/ImpactLoss))
|
||||
as a native Elastix metric. The parameter map is the MIND + TotalSegmentator
|
||||
**static** feature combination of the `MR_CT_TS` reference map in the
|
||||
[`VBoussot/ImpactReg`](https://huggingface.co/VBoussot/ImpactReg) repo, with a
|
||||
**rigid 6-DOF `EulerTransform`** in place of the reference's RecursiveBSpline
|
||||
(its bending-energy penalty is zero for a rigid transform, so it is dropped) —
|
||||
i.e. the reference metric/budget, rigid-only, on the GPU.
|
||||
|
||||
Prerequisites (check with `RP.availability()`; a missing piece raises
|
||||
`PipelineUnavailable` with the exact command):
|
||||
|
||||
```bash
|
||||
# in the longitudinal-313 env (Python >= 3.11):
|
||||
pip install impact-reg-konfai # CLI + KonfAI runtime (pulls h5py etc.)
|
||||
bash scripts/setup_impact_konfai.sh # one-time, this host only: the elastix shim
|
||||
# 1. Docker (with NVIDIA container toolkit so --gpus works)
|
||||
docker --version
|
||||
|
||||
# 2. Pull the image (~38 GB, one-time)
|
||||
docker pull vboussot/elastix_impact
|
||||
|
||||
# 3. Feature models under the cache (the pipeline prints the exact
|
||||
# curl commands if any are missing):
|
||||
# data/impact_models/MIND/R1D2_3D.pt (~15 KB)
|
||||
# data/impact_models/TS/M852.pt (~200 MB)
|
||||
# data/impact_models/TS/M850.pt (~200 MB)
|
||||
# (from HF VBoussot/impact-torchscript-models, or `sh
|
||||
# Data/Models/download_models.sh` from the ImpactLoss repo)
|
||||
```
|
||||
|
||||
The CLI then auto-downloads the one-time assets on first `register`: the
|
||||
preset app (HF `VBoussot/ImpactReg`), the TorchScript feature models (HF
|
||||
`VBoussot/impact-torchscript-models`: MIND ~15 KB + TS/M730 ~200 MB) and the
|
||||
**elastix-impact 1.0.0** binary (GitHub `vboussot/ImpactElastix`, ~40 MB)
|
||||
into `~/.cache` (HF cache + `~/.cache/konfai`).
|
||||
### How the adapter drives the image
|
||||
|
||||
### Why the elastix shim (this host only)
|
||||
|
||||
The stock binary needs **glibc ≥ 2.35** and links **LibTorch 2.8.0**; this
|
||||
host (Ubuntu 20.04, glibc 2.31, env torch 2.14) satisfies neither, so
|
||||
`scripts/setup_impact_konfai.sh` builds a self-contained shim at
|
||||
`~/.cache/konfai/elastix-impact-shim`: the glibc 2.35 loader + libs extracted
|
||||
from the `niftyreg:2.1.1-ubuntu` image (docker is needed once for that copy
|
||||
only), the self-contained LibTorch 2.8.0 cu128 from download.pytorch.org
|
||||
(~3.8 GB zip), and a wrapper `bin/elastix` that runs the stock binary through
|
||||
the shim loader with the LibTorch 2.8.0 library path (stripping the inherited
|
||||
`LD_LIBRARY_PATH`, so the env's torch 2.14 cannot leak in). The pipeline
|
||||
selects the shim via `KONFAI_ELASTIX_DIR` (`_impact_elastix_root`); a host
|
||||
new enough to run the stock binary directly needs no shim at all.
|
||||
|
||||
### How the adapter drives the CLI
|
||||
|
||||
1. **Full volumes, fixed=T1c (MR), moving=CT** — the ImpactReg **MR_CT**
|
||||
convention, which coincides with the shared rule (the T1c is the fixed
|
||||
image): the preset's output is a displacement field on the **fixed** grid
|
||||
mapping fixed→moving, so the field is the contract transform T1c→CT
|
||||
**directly** (displacement fields cannot be inverted with `GetInverse`,
|
||||
which a fixed=CT direction would require). It also keeps
|
||||
the metric's sample-validity gate healthy: samples drawn from the tight
|
||||
T1c head crop (~220×220×150 mm) map mostly inside the large CT head-neck
|
||||
FOV (~300×300×251 mm), unlike fixed=CT where only ~14% were in-buffer at
|
||||
the auto-init.
|
||||
2. **M6 robustness overrides** — the preset's map is tuned for well-aligned
|
||||
pairs, so the adapter forwards raw elastix lines through the package's
|
||||
`parameter_overrides` knob: `AutomaticTransformInitialization=true` +
|
||||
`AutomaticTransformInitializationMethod=CenterOfGravity` (the preset's
|
||||
GeometricalCenter start leaves the intensity centroids ~50-100 mm apart on
|
||||
the head+neck FOV mismatch) and `RequiredRatioOfValidSamples 0.1` (build
|
||||
default 0.25; keeps the divergence gate while letting the FOV-mismatched
|
||||
runs proceed).
|
||||
3. **`--fields-only`** — the adapter derives the warp itself (the package's
|
||||
derived "Moved" image would be an extra full-size resample of the same
|
||||
content).
|
||||
4. **GPU auto-spread** across the host GPUs by pid (`--impact-gpu -1` forces
|
||||
CPU; the engine then turns mixed precision off).
|
||||
5. **Read-back** — the transform comes from `<out>/P000/Transform.h5` (an
|
||||
ITK-form HDF5 `DisplacementFieldTransform` on the T1c grid; the SimpleITK
|
||||
build here has no ITK HDF5 transform reader, so it is parsed with h5py)
|
||||
and direction-checked by `_orient_impact`: the skull-ring gap of the field
|
||||
vs a sign-flipped (first-order reverse) proxy — the `fix_orientation`
|
||||
analogue for a field with no `GetInverse`.
|
||||
6. **Intensity normalization happens inside the metric / model** (no
|
||||
1. **Full volumes, fixed=T1c (MR), moving=CT** — the shared fixed-image rule:
|
||||
the metric draws its random spatial samples from the fixed buffer and
|
||||
evaluates them where they map inside the moving buffer. With the tight T1c
|
||||
head crop (~220×220×150 mm) as fixed, the samples map mostly inside the
|
||||
large CT head-neck FOV (~300×300×251 mm), so the
|
||||
`RequiredRatioOfValidSamples 0.1` gate (build default 0.25) rarely fires.
|
||||
2. **Parameter map** (the rigid `MR_CT_TS` reference config):
|
||||
`(Metric "Impact" "AdvancedMattesMutualInformation")` with weights
|
||||
1.0 / 0.4; per level (coarse → fine) the **MIND** handcrafted descriptor
|
||||
(`MIND/R1D2_3D.pt`, L1, subset 32, layer mask `"1"`) combined with
|
||||
**TotalSegmentator** `TS/M852.pt` (levels 0-1) / `TS/M850.pt` (levels
|
||||
2-3, Dice, subset 64, deepest layers), layer weights 0.2/0.8, 0.3/0.7,
|
||||
0.6/0.4, 0.7/0.3, at ImpactVoxelSize 6/3/2/2 mm (scaled to the moving
|
||||
spacing); **Static** mode (whole-image features, no backprop); no
|
||||
image-resampling pyramid (all-1 schedules — the scale is carried by
|
||||
ImpactVoxelSize, per the IMPACT docs); `RandomCoordinate` sampler; ASGD
|
||||
with per-level MaximumNumberOfIterations 400/300/200/200 and
|
||||
NumberOfSpatialSamples 2000; `EulerTransform` +
|
||||
`AutomaticTransformInitialization` (CenterOfGravity) +
|
||||
`AutomaticScalesEstimation`. A copy of the exact map is dumped to
|
||||
`data/m6reg/impact/paramdump/` per run for inspection.
|
||||
3. **GPU auto-spread** across the host GPUs by pid (`docker run --gpus all`,
|
||||
`ImpactGPU` = the worker's device; `--impact-gpu -1` forces CPU and turns
|
||||
mixed precision off).
|
||||
4. **Read-back** — the transform comes from
|
||||
`Out/TransformParameters.0.itk.txt` (an `Euler3DTransform` in the world
|
||||
physical frame; the full combination transform, with the auto-init folded
|
||||
in by elastix) and is direction-checked by `fix_orientation` against the
|
||||
skull ring (with fixed=T1c / moving=CT the gate keeps the file's transform,
|
||||
`auto_inverted: False`).
|
||||
5. **Intensity normalization happens inside the metric / models** (no
|
||||
pipeline-side normalization): the MIND descriptor normalizes each 5-voxel
|
||||
neighbourhood to [0, 1] locally (modality-agnostic by construction); the
|
||||
segmentation models (TS/MRSeg) are standardized to zero mean / unit
|
||||
variance per the model card. Pre-normalizing the volumes would be an
|
||||
exact no-op for the z-scored models and would only corrupt the HU scale
|
||||
used by MI scoring and the QA brain-window images.
|
||||
segmentation models are standardized to zero mean / unit variance per the
|
||||
model card. Pre-normalizing the volumes would be an exact no-op for the
|
||||
z-scored models and would only corrupt the HU scale used by MI scoring and
|
||||
the QA brain-window images.
|
||||
|
||||
## Notes / caveats
|
||||
|
||||
|
|
@ -598,22 +582,20 @@ new enough to run the stock binary directly needs no shim at all.
|
|||
dataset actually ships with.
|
||||
- `synthmorph` is deterministic (one fixed pretrained model, no random
|
||||
subsampling), so its per-case numbers are stable run to run — unlike ANTs /
|
||||
IMPACT. Its low MI (median 0.238; 0.06–0.10 on the repositioned cases) is a
|
||||
genuine cross-modality / tilt weakness, not variance.
|
||||
- `fix_orientation` (elastix/ants/niftyreg/synthmorph) chooses between a
|
||||
transform and its inverse by skull-ring concentricity — a correctness gate
|
||||
IMPACT. Its low MI (median 0.277 with the default CT clip; 0.06–0.11 on the
|
||||
repositioned cases) is a genuine cross-modality / tilt weakness, not
|
||||
variance.
|
||||
- `fix_orientation` (elastix/ants/impact/niftyreg/synthmorph) chooses between
|
||||
a transform and its inverse by skull-ring concentricity — a correctness gate
|
||||
that makes the result independent of each toolkit's transform-direction
|
||||
convention, so the fixed/moving swap is absorbed by the gate rather than
|
||||
re-derived per toolkit. Under the shared fixed=T1c / moving=CT rule the raw
|
||||
transforms come out in the contract direction for elastix (combination
|
||||
transform = fixed→moving), antspyx (ANTs forward transform) and synthmorph
|
||||
(the `-T` LTA, model estimates first→second) — completed runs report
|
||||
`auto_inverted: False` for those; `niftyreg`'s `-aff` file comes out the
|
||||
other way and the gate inverts it (8/9 `auto_inverted: True`). `impact`
|
||||
returns a displacement field (no `GetInverse` in ITK), so it uses
|
||||
`_orient_impact` — the same skull-ring decision against a sign-flipped
|
||||
(first-order reverse) proxy field; its MR_CT field is in the contract
|
||||
direction (`auto_inverted: False`).
|
||||
transform = fixed→moving), impact (its `TransformParameters.0.itk.txt`,
|
||||
the full combination Euler transform) and antspyx (ANTs forward transform)
|
||||
and synthmorph (the `-T` LTA, model estimates first→second) — completed
|
||||
runs report `auto_inverted: False` for those; `niftyreg`'s `-aff` file
|
||||
comes out the other way and the gate inverts it (8/9 `auto_inverted: True`).
|
||||
- **SynthMorph specifics** (Docker `freesurfer/synthmorph`, TensorFlow, GPU):
|
||||
- **Cross-modality / tilt weakness.** The `rigid` model is trained on brain
|
||||
*MRI*, so on these CT↔T1c pairs it recovers translation but is repeatedly
|
||||
|
|
@ -626,8 +608,8 @@ new enough to run the stock binary directly needs no shim at all.
|
|||
path).
|
||||
- **GPU sharing.** The container runs as the host uid with
|
||||
`TF_FORCE_GPU_ALLOW_GROWTH` so concurrent benchmark workers share a GPU.
|
||||
- **~25 s/case** (GPU: one pretrained forward pass), between SimpleITK
|
||||
(~10 s) / ANTs (~37 s) and IMPACT (~216 s, deformable preset).
|
||||
- **~28 s/case** (GPU: one pretrained forward pass), between SimpleITK
|
||||
(~11 s) / ANTs (~17 s) and IMPACT (~115 s, rigid docker config).
|
||||
- **NiftyReg specifics** (conda-forge `niftyreg` 1.5.69, `reg_aladin`,
|
||||
symmetric block matching + TLS, CPU/OpenMP):
|
||||
- **Different algorithmic family.** No histogram MI objective: 4-voxel
|
||||
|
|
@ -676,51 +658,39 @@ new enough to run the stock binary directly needs no shim at all.
|
|||
- Benchmark budget: `MaximumNumberOfIterations` 2000 per resolution
|
||||
(ASGD 2000/2000/2000 over 3 resolutions) with the sampler/metric at their
|
||||
defaults (`NumberOfSpatialSamples` 5000, `NumberOfHistogramBins` 32).
|
||||
- **IMPACT specifics** (`impact-reg-konfai` 1.8.6, KonfAI runtime,
|
||||
elastix-impact 5.2, GPU; preset `Elastix_IMPACT_Static`):
|
||||
- **IMPACT specifics** (Docker `vboussot/elastix_impact`, Elastix 5.2 +
|
||||
IMPACT metric, GPU; rigid MIND+TS static config of the `MR_CT_TS` reference
|
||||
map):
|
||||
- **Runs on M6 with the T1c-fixed direction + relaxed sample-validity
|
||||
gate.** Full volumes, no mask, no crop / pad, fixed=T1c/moving=CT (the
|
||||
shared rule; the ImpactReg MR_CT convention coincides with it): the
|
||||
preset's output field is on the fixed grid, so that direction is the
|
||||
contract transform (T1c→CT) directly, and the metric's samples (drawn
|
||||
from the tight T1c crop) map mostly inside the large CT FOV. The package's generated map still defaults to
|
||||
`RequiredRatioOfValidSamples 0.25`, so the adapter overrides it to 0.1
|
||||
(a genuinely diverging transform, <10% in-buffer, still aborts). In the
|
||||
old Docker era (fixed=CT) the gate aborted the no-mask M6 pairs until
|
||||
the same 0.1 relaxation. The transform contract holds; `_orient_impact`
|
||||
decides the direction as usual (completed runs report
|
||||
`auto_inverted: False` — the MR_CT field is already the contract).
|
||||
- **Auto init.** `AutomaticTransformInitialization` (CenterOfGravity) is
|
||||
applied as an override (the preset's GeometricalCenter start is too
|
||||
crude for the head+neck FOV mismatch); no manual translation is used.
|
||||
- **Deformable, not rigid.** The preset's native transform is a
|
||||
RecursiveBSpline (FinalGridSpacing 14 mm, 4 scales) regularized by the
|
||||
bending-energy metric; the old pipeline forced a rigid `EulerTransform`
|
||||
in place of it. The benchmark contract (warp the CT concentric with the
|
||||
MR head) is transform-agnostic, so this is a native-config comparison
|
||||
point like every other row.
|
||||
shared rule): the metric's samples (drawn from the tight T1c head crop)
|
||||
map mostly inside the large CT head-neck FOV, so the gate rarely fires.
|
||||
The map still sets `RequiredRatioOfValidSamples 0.1` (build default
|
||||
0.25) as a safety override — a genuinely diverging transform, <10%
|
||||
in-buffer, still aborts. The combination Euler transform comes out in
|
||||
the contract direction (completed runs report `auto_inverted: False`).
|
||||
- **Auto init.** `AutomaticTransformInitialization` (CenterOfGravity)
|
||||
aligns the intensity centroids (no manual translation); the reference
|
||||
map's GeometricalCenter start is too crude for the head+neck FOV
|
||||
mismatch.
|
||||
- **Rigid, like every other pipeline.** The reference map's
|
||||
RecursiveBSpline + bending-energy regularizer is replaced by a 6-DOF
|
||||
`EulerTransform` (the bending penalty is zero for a rigid transform).
|
||||
- **History: single-init convergence.** The semantic landscape has more
|
||||
local minima than Mattes-MI; earlier (masked, fixed=CT) runs no longer
|
||||
nailed the hardest repositioned case (`m6_22IRRKU6` mid 22.7 / std 27.0;
|
||||
the pre-mask head-box flow got it to mid 8.3 / std 13.2). The no-mask
|
||||
MRSegmentator runs showed the same pattern (0.635 MI on `m6_22IRRKU6`
|
||||
at 2000/5000 but mid 26.1 / std 28.6 there; accepted on `m6_2BBN5XA4`,
|
||||
mid 15.1 / std 10.7). The single CenterOfGravity start on the ~30°
|
||||
tilt pairs remains IMPACT's limiting factor — a multi-start (or
|
||||
`--tta` flips through the preset app) would be the next step.
|
||||
- **Stochastic.** The Jacobian preset (`Elastix_IMPACT_Jacobian`)
|
||||
resamples patches through the network each iteration, so results vary
|
||||
run to run; the default Static preset re-samples coordinates each
|
||||
iteration but the feature maps are deterministic given the pose (see
|
||||
Stochasticity).
|
||||
- **The elastix shim (this host).** The stock binary needs glibc ≥ 2.35
|
||||
+ LibTorch 2.8.0; the host (glibc 2.31, torch 2.14) runs it through
|
||||
`~/.cache/konfai/elastix-impact-shim` (built by
|
||||
`scripts/setup_impact_konfai.sh`, selected via `KONFAI_ELASTIX_DIR`).
|
||||
Same glibc story as the niftyreg v2.x binaries.
|
||||
- **Models / presets are not in the repo.** They auto-download from HF
|
||||
(`VBoussot/ImpactReg`, `VBoussot/impact-torchscript-models`) into
|
||||
`~/.cache/huggingface` on first use; the old `data/impact_models/`
|
||||
cache is obsolete.
|
||||
- **CPU fallback:** `--impact-gpu -1` (no `--gpu` flag; the engine runs
|
||||
the feature models on CPU with mixed precision off — much slower).
|
||||
local minima than Mattes-MI. The T1c-fixed direction fixes most of the
|
||||
hard cases (the repositioned `m6_22IRRKU6` / `m6_23XIMO4M` now register
|
||||
as well as the MI tools do), but the single CenterOfGravity start on the
|
||||
~30° tilt pair `m6_2BBN5XA4` remains a weakness — a multi-start tilt
|
||||
grid (as production SimpleITK uses) would be the next step.
|
||||
- **Stochastic.** The config is **Static** mode: it re-samples spatial
|
||||
coordinates every iteration, but the feature maps are deterministic
|
||||
given the pose (see Stochasticity). The older Jacobian-mode configs
|
||||
(docker-era experiments, archived in `results/bench_registration/impact_*.json`)
|
||||
resampled patches through the network each iteration, so their
|
||||
per-case numbers are one sample from a distribution.
|
||||
- **Models are under the data root, not the repo.** The TorchScript
|
||||
feature models live in `data/impact_models/` (MIND ~15 KB + TS ~200 MB
|
||||
each, from HF `VBoussot/impact-torchscript-models`); the pipeline
|
||||
raises with the exact `curl` commands if one is missing.
|
||||
- **CPU fallback:** `--impact-gpu -1` (`ImpactGPU -1`, mixed precision
|
||||
off — much slower).
|
||||
|
|
@ -11,9 +11,10 @@ higher = better in-sync = better aligned. The production geometric QA
|
|||
(scripts/02_build_m6_dataset.py) is still recorded per case in the JSON:
|
||||
|
||||
- mi_ct_mr : Mattes MI (nats), warped-CT ~ MR over the MR head
|
||||
(the reported alignment score).
|
||||
- tv_in_ct_frac : fraction of the (dilated) warped GTV that falls inside
|
||||
the warped-CT head tissue (the slab must straddle it).
|
||||
(the reported alignment score, higher = better).
|
||||
- impact_loss : IMPACT semantic loss (vboussot/ImpactLoss; TotalSeg
|
||||
M730 deep features, weighted L1), warped-CT ~ MR over
|
||||
the full co-registered volumes (lower = better).
|
||||
- tv_nvox : warped GTV volume (>0.5 voxels).
|
||||
- skull_ring_gap_mm : concentricity of the warped-CT skull ring vs the MR
|
||||
head silhouette (mid/low level + angular std).
|
||||
|
|
@ -31,10 +32,11 @@ whichever conda env has a given toolkit installed, and it records only the
|
|||
pipelines that are importable there. elastix runs in the main env through the
|
||||
ITK-Elastix python API (pip ``itk`` + ``itk-elastix``); niftyreg runs natively
|
||||
(``conda install -c conda-forge niftyreg`` provides ``reg_aladin``);
|
||||
``impact`` runs the ``impact-reg-konfai`` package (pip; GPU; one-time elastix
|
||||
shim setup on hosts too old for the stock binary — see
|
||||
REGISTRATION_BENCHMARK.md); ``synthmorph`` runs in a Docker image
|
||||
(``freesurfer/synthmorph``; needs Docker + the pulled image). The `--merge`
|
||||
``impact`` runs the ``vboussot/elastix_impact`` Docker image (GPU; the rigid
|
||||
MIND + TotalSegmentator static config, needs Docker + the pulled image + the
|
||||
model assets — see REGISTRATION_BENCHMARK.md); ``synthmorph`` runs in a Docker
|
||||
image (``freesurfer/synthmorph``; needs Docker + the pulled image). The
|
||||
`--merge`
|
||||
mode then combines the per-pipeline JSON files (accumulated across envs) into
|
||||
one summary + plot.
|
||||
|
||||
|
|
@ -99,6 +101,54 @@ def _m6():
|
|||
return RP._load_m6()
|
||||
|
||||
|
||||
# the ImpactLoss scoring model (the repo's default baseline for MR/CT)
|
||||
IMPACT_SCORE_MODEL = "TS/M730.pt"
|
||||
_IMPACT_LOSS_STATE = None
|
||||
|
||||
|
||||
def _impact_loss_scorer():
|
||||
"""Lazily build the ImpactLoss semantic-loss scorer (once per process) on
|
||||
the GPU auto-picked for this worker. Returns ``(fn, device)`` or None if
|
||||
torch / the model are unavailable (the score is then omitted)."""
|
||||
global _IMPACT_LOSS_STATE
|
||||
if _IMPACT_LOSS_STATE is None:
|
||||
try:
|
||||
import warnings
|
||||
import torch
|
||||
from src.impact_loss import IMPACTReg
|
||||
ngpu = torch.cuda.device_count() if torch.cuda.is_available() else 0
|
||||
if ngpu:
|
||||
torch.cuda.set_device(os.getpid() % ngpu)
|
||||
with warnings.catch_warnings():
|
||||
# torch.jit.load is marked deprecated by upstream torch 2.14;
|
||||
# the IMPACT models are TorchScript and jit.load is required
|
||||
warnings.simplefilter("ignore", FutureWarning)
|
||||
fn = IMPACTReg(IMPACT_SCORE_MODEL, shape=[0, 0, 0], in_channels=1,
|
||||
weights=[1.0, 1.0], model_cache=d("data/impact_models"))
|
||||
_IMPACT_LOSS_STATE = (fn, torch.device("cuda" if ngpu else "cpu"))
|
||||
except Exception: # noqa
|
||||
_IMPACT_LOSS_STATE = False
|
||||
return _IMPACT_LOSS_STATE or None
|
||||
|
||||
|
||||
def _impact_loss_value(warped_ct, mr):
|
||||
"""IMPACT semantic loss (lower = more in-sync) between the co-registered
|
||||
pair: the CT inverse-transformed into MR coordinates vs the original MR,
|
||||
over the full volumes (the IMPACT models standardize intensity inside
|
||||
the metric, so both go in raw)."""
|
||||
st = _impact_loss_scorer()
|
||||
if st is None:
|
||||
return None
|
||||
fn, dev = st
|
||||
import torch
|
||||
a = torch.as_tensor(np.ascontiguousarray(warped_ct, dtype="float32"),
|
||||
device=dev).unsqueeze(0).unsqueeze(0)
|
||||
b = torch.as_tensor(np.ascontiguousarray(mr, dtype="float32"),
|
||||
device=dev).unsqueeze(0).unsqueeze(0)
|
||||
with torch.no_grad():
|
||||
return float(fn(a, b).item())
|
||||
|
||||
|
||||
def _clip_image(img, lo, hi):
|
||||
"""Clip image intensities to [lo, hi] (geometry preserved)."""
|
||||
a = np.clip(sitk.GetArrayFromImage(img).astype("float32"), lo, hi)
|
||||
|
|
@ -153,10 +203,12 @@ def evaluate(ct, t1c, tv, T):
|
|||
warped_ct = sitk.GetArrayFromImage(sitk.Resample(ct, t1c, T, sitk.sitkLinear))
|
||||
warped_tv = sitk.GetArrayFromImage(sitk.Resample(tv, t1c, T, sitk.sitkNearestNeighbor, 0.0))
|
||||
gap = m6._skull_ring_gap(warped_ct, ha)
|
||||
# achieved-metric alignment score: Mattes MI between the CT inverse-transformed
|
||||
# into MR coordinates (warped_ct) and the original MR (ha), over the MR head.
|
||||
# achieved-metric alignment scores: Mattes MI between the CT
|
||||
# inverse-transformed into MR coordinates (warped_ct) and the original MR
|
||||
# (ha), over the MR head; IMPACT semantic loss over the full volumes.
|
||||
ha_f = ha.astype("float32", copy=False)
|
||||
mi = _mi_masked(warped_ct, ha_f, head_mask_from_image(ha_f))
|
||||
il = _impact_loss_value(warped_ct, ha_f)
|
||||
posw = warped_ct[warped_ct > 0]
|
||||
tissue_w = (warped_ct > max(2.0, float(np.percentile(posw, 2)))) if posw.size \
|
||||
else np.zeros_like(warped_ct, bool)
|
||||
|
|
@ -171,6 +223,7 @@ def evaluate(ct, t1c, tv, T):
|
|||
return {
|
||||
"skull_ring_gap_mm": gap,
|
||||
"mi_ct_mr": round(mi, 4) if mi is not None else None,
|
||||
"impact_loss": round(il, 4) if il is not None else None,
|
||||
"tv_nvox": nvox,
|
||||
"tv_in_ct_frac": round(tv_in_ct_frac, 3),
|
||||
"alignment": "concentric" if ok_gap else "not_concentric",
|
||||
|
|
@ -248,8 +301,9 @@ def _call(pipeline, ct, t1c, tv, args):
|
|||
gpu=args.synthmorph_gpu, threads=args.threads,
|
||||
extent=args.synthmorph_extent)
|
||||
if pipeline == "impact":
|
||||
return RP.register_impact(ct, t1c, preset=args.impact_preset, gpu=args.impact_gpu,
|
||||
iters=args.impact_iters, samples=args.impact_samples)
|
||||
return RP.register_impact(ct, t1c, gpu=args.impact_gpu,
|
||||
iters=args.impact_iters or RP.IMPACT_DEFAULT_ITERS,
|
||||
samples=args.impact_samples or RP.IMPACT_DEFAULT_SAMPLES)
|
||||
if pipeline == "niftyreg":
|
||||
return RP.register_niftyreg(ct, t1c, threads=args.threads,
|
||||
backend=args.niftyreg_backend,
|
||||
|
|
@ -268,6 +322,7 @@ def _run_case(pipeline, case, args):
|
|||
out = {"key": key, "subject": case["subject"], "date": case["date"]}
|
||||
if not ct_complete(ct):
|
||||
out.update({"skull_ring_gap_mm": None, "tv_nvox": 0, "tv_in_ct_frac": None,
|
||||
"impact_loss": None,
|
||||
"alignment": "ct_incomplete", "accepted": False,
|
||||
"note": "CT head incomplete (data issue, not a pipeline failure)"})
|
||||
return out
|
||||
|
|
@ -313,7 +368,7 @@ def run_pipeline(pipeline, cases, args):
|
|||
r = results[-1]
|
||||
reg = f"reg={r.get('reg_wall_s')}s" if r.get("reg_wall_s") is not None else "incomplete-CT"
|
||||
print(f" {c['key']}: mi={r.get('mi_ct_mr')} "
|
||||
f"tv_in_ct={r.get('tv_in_ct_frac')} {reg}", flush=True)
|
||||
f"impact_loss={r.get('impact_loss')} {reg}", flush=True)
|
||||
except Exception as e: # noqa
|
||||
errors.append({"key": c["key"], "error": repr(e)})
|
||||
print(f" {c['key']}: ERROR {e!r}", flush=True)
|
||||
|
|
@ -325,7 +380,7 @@ def run_pipeline(pipeline, cases, args):
|
|||
gaps_mid = [r["skull_ring_gap_mm"]["mid_mm"] for r in registered if r.get("skull_ring_gap_mm")]
|
||||
gaps_std = [r["skull_ring_gap_mm"]["std_mm"] for r in registered if r.get("skull_ring_gap_mm")]
|
||||
regt = [r["reg_wall_s"] for r in registered]
|
||||
tvfr = [r["tv_in_ct_frac"] for r in registered if r.get("tv_in_ct_frac") is not None]
|
||||
ilv = [r["impact_loss"] for r in registered if r.get("impact_loss") is not None]
|
||||
mifr = [r["mi_ct_mr"] for r in registered if r.get("mi_ct_mr") is not None]
|
||||
first = (registered or results)[0] if (registered or results) else {}
|
||||
row = {
|
||||
|
|
@ -341,7 +396,8 @@ def run_pipeline(pipeline, cases, args):
|
|||
"median": round(float(np.median(gaps_mid)), 1)} if gaps_mid else None,
|
||||
"skull_ring_gap_std_mm": {"mean": round(float(np.mean(gaps_std)), 1),
|
||||
"median": round(float(np.median(gaps_std)), 1)} if gaps_std else None,
|
||||
"tv_in_ct_frac": {"mean": round(float(np.mean(tvfr)), 3)} if tvfr else None,
|
||||
"impact_loss": {"mean": round(float(np.mean(ilv)), 4),
|
||||
"median": round(float(np.median(ilv)), 4)} if ilv else None,
|
||||
"mi_ct_mr": {"mean": round(float(np.mean(mifr)), 4),
|
||||
"median": round(float(np.median(mifr)), 4)} if mifr else None,
|
||||
"cases": results, "errors": errors,
|
||||
|
|
@ -368,24 +424,24 @@ def _fmt(v, spec="{}", default="-"):
|
|||
|
||||
def print_table(rows):
|
||||
order = [p for p in RP.ALL_PIPELINES if any(r["pipeline"] == p for r in rows)]
|
||||
print("\n" + "=" * 92)
|
||||
print("\n" + "=" * 96)
|
||||
hdr = (f"{'pipeline':10s} {'avail':6s} {'toolkit':22s} {'n':>3s} {'ok':>3s} {'err':>4s} "
|
||||
f"{'s/reg':>7s} {'tvInCT':>7s} {'MI':>7s}")
|
||||
f"{'s/reg':>7s} {'ImpactLoss':>10s} {'MI':>7s}")
|
||||
print(hdr)
|
||||
print("-" * 92)
|
||||
print("-" * 96)
|
||||
for p in order:
|
||||
r = next(x for x in rows if x["pipeline"] == p)
|
||||
if not r.get("available"):
|
||||
print(f"{p:10s} {('no'):6s} {'-':22s} {r['n_cases']:>3d} {0:>3d} {'-':>4s} "
|
||||
f"{'-':>7s} {'-':>7s} {'-':>7s} [{r['reason']}]")
|
||||
f"{'-':>7s} {'-':>10s} {'-':>7s} [{r['reason']}]")
|
||||
continue
|
||||
rt = (r.get("reg_wall_s") or {}).get("median")
|
||||
tv = (r.get("tv_in_ct_frac") or {}).get("mean")
|
||||
il = (r.get("impact_loss") or {}).get("median")
|
||||
mi = (r.get("mi_ct_mr") or {}).get("median")
|
||||
print(f"{p:10s} {'yes':6s} {(r.get('toolkit') or '')[:22]:22s} {r['n_cases']:>3d} "
|
||||
f"{r.get('n_registered', 0):>3d} {r['n_error']:>4d} {_fmt(rt, '{:.1f}'):>7s} "
|
||||
f"{_fmt(tv, '{:.2f}'):>7s} {_fmt(mi, '{:.3f}'):>7s}")
|
||||
print("=" * 92)
|
||||
f"{_fmt(il, '{:.3f}'):>10s} {_fmt(mi, '{:.3f}'):>7s}")
|
||||
print("=" * 96)
|
||||
|
||||
|
||||
def write_plot(rows, out_png):
|
||||
|
|
@ -402,13 +458,15 @@ def write_plot(rows, out_png):
|
|||
return
|
||||
labels = list(ok_rows)
|
||||
regt = [(ok_rows[p].get("reg_wall_s") or {}).get("median") or 0 for p in labels]
|
||||
il = [(ok_rows[p].get("impact_loss") or {}).get("median") or 0 for p in labels]
|
||||
mi = [(ok_rows[p].get("mi_ct_mr") or {}).get("median") or 0 for p in labels]
|
||||
fig, axes = plt.subplots(1, 2, figsize=(10, 4))
|
||||
colors = ["#4878CF", "#6ACC65"]
|
||||
fig, axes = plt.subplots(1, 3, figsize=(14, 4))
|
||||
colors = ["#6ACC65", "#F5A623", "#4878CF"]
|
||||
for ax, (title, vals, fmt, ylab) in zip(axes, [
|
||||
("Registration time (s, median)", regt, "{:.0f}", "s"),
|
||||
("ImpactLoss: warped-CT ~ MR (semantic, median; lower = better)", il, "{:.3f}", "loss"),
|
||||
("Mattes MI: warped-CT ~ MR (nats, median)", mi, "{:.3f}", "nats")]):
|
||||
bars = ax.bar(range(len(vals)), vals, color=colors[:len(vals)])
|
||||
bars = ax.bar(range(len(vals)), vals, color=colors[0])
|
||||
for b, v in zip(bars, vals):
|
||||
ax.annotate(fmt.format(v), (b.get_x() + b.get_width() / 2, b.get_height()),
|
||||
ha="center", va="bottom", fontsize=8)
|
||||
|
|
@ -438,7 +496,9 @@ def merge(args):
|
|||
summary = {
|
||||
"timestamp": datetime.now().isoformat(timespec="seconds"),
|
||||
"protocol": ("rigid CT->T1c; cross-modality; alignment scored by achieved Mattes MI "
|
||||
"(nats, warped-CT ~ MR over the MR head, higher = better); reg time "
|
||||
"(nats, warped-CT ~ MR over the MR head, higher = better) and the "
|
||||
"IMPACT semantic loss (vboussot/ImpactLoss, TS/M730 features, "
|
||||
"warped-CT ~ MR over the full volumes, lower = better); reg time "
|
||||
"excludes I/O+scoring; per-case JSON retains the production skull-ring "
|
||||
"gap / acceptance QA; best-of-T vs T^-1 orientation gate for the "
|
||||
"elastix-style toolkits"),
|
||||
|
|
@ -452,7 +512,7 @@ def merge(args):
|
|||
print("summary:", sf)
|
||||
write_plot(rows, os.path.join(out_dir, "bench_registration.png"))
|
||||
# markdown table for the report
|
||||
md = ["| pipeline | available | toolkit | cases | ok | err | s/reg (median) | tv-in-CT | MI (nats, median) |",
|
||||
md = ["| pipeline | available | toolkit | cases | ok | err | s/reg (median) | ImpactLoss (median) | MI (nats, median) |",
|
||||
"|---|---|---|---|---|---|---|---|---|"]
|
||||
for p in [x["pipeline"] for x in rows]:
|
||||
r = next(x for x in rows if x["pipeline"] == p)
|
||||
|
|
@ -463,7 +523,7 @@ def merge(args):
|
|||
f"| {p} | yes | {(r.get('toolkit') or '')} | {r['n_cases']} | {r.get('n_registered', 0)} | "
|
||||
f"{r['n_error']} | "
|
||||
f"{(r.get('reg_wall_s') or {}).get('median')} | "
|
||||
f"{(r.get('tv_in_ct_frac') or {}).get('mean')} | "
|
||||
f"{(r.get('impact_loss') or {}).get('median')} | "
|
||||
f"{(r.get('mi_ct_mr') or {}).get('median')} |")
|
||||
mf = os.path.join(out_dir, "summary.md")
|
||||
with open(mf, "w") as f:
|
||||
|
|
@ -496,24 +556,16 @@ def parse_args():
|
|||
ap.add_argument("--synthmorph-extent", type=int, default=256,
|
||||
help="SynthMorph isotropic registration-space extent in mm "
|
||||
"(192 or 256; default 256)")
|
||||
ap.add_argument("--impact-preset", default=RP.IMPACT_DEFAULT_PRESET,
|
||||
help="ImpactReg preset from the published database "
|
||||
f"(default: {RP.IMPACT_DEFAULT_PRESET}; e.g. "
|
||||
"Elastix_IMPACT_Jacobian, Generic_Rigid, "
|
||||
"Generic_Rigid_BSpline, FireANTs_SyN, FireANTs_IMPACT, "
|
||||
"ConvexAdam_Coarse/Composite; list live with: "
|
||||
"impact-reg-konfai register --help)")
|
||||
ap.add_argument("--impact-gpu", type=int, default=None,
|
||||
help="GPU device index for IMPACT (default: auto-spread over "
|
||||
"the host GPUs by pid; -1 forces CPU)")
|
||||
ap.add_argument("--impact-iters", type=int, default=None,
|
||||
help="preset max_iterations override, one value for all "
|
||||
"resolutions (only effective for presets without a "
|
||||
"per-resolution model matrix; default: the preset's "
|
||||
"own budget, 400/300/200/200 for Elastix_IMPACT_Static)")
|
||||
help="IMPACT MaximumNumberOfIterations, one value for all "
|
||||
"levels (default: the reference map's per-level budget "
|
||||
f"{RP.IMPACT_DEFAULT_ITERS})")
|
||||
ap.add_argument("--impact-samples", type=int, default=None,
|
||||
help="preset spatial_samples override (NumberOfSpatialSamples; "
|
||||
"default: the preset's own value, 2000)")
|
||||
help="IMPACT NumberOfSpatialSamples per iteration (default: "
|
||||
f"{RP.IMPACT_DEFAULT_SAMPLES})")
|
||||
ap.add_argument("--niftyreg-backend", choices=["auto", "conda", "docker"],
|
||||
default="auto",
|
||||
help="niftyreg reg_aladin build: docker = official v2.1.1 in the "
|
||||
|
|
|
|||
|
|
@ -1,110 +0,0 @@
|
|||
#!/usr/bin/env bash
|
||||
# One-time host setup for the IMPACT registration pipeline (impact-reg-konfai).
|
||||
#
|
||||
# The stock elastix-impact 1.0.0 binary (vboussot/ImpactElastix) needs
|
||||
# glibc >= 2.35 and links LibTorch 2.8.0. This host (Ubuntu 20.04, glibc 2.31,
|
||||
# env torch 2.14) satisfies neither, so this script builds a self-contained
|
||||
# shim at ~/.cache/konfai/elastix-impact-shim:
|
||||
# * glibc 2.35 loader + libs, extracted from the niftyreg:2.1.1-ubuntu image
|
||||
# (ubuntu 22.04 base) -- docker is needed ONLY for this one-time copy;
|
||||
# * LibTorch 2.8.0 cu128, self-contained (its own CUDA runtime), from
|
||||
# download.pytorch.org;
|
||||
# * a wrapper bin/elastix that runs the stock binary through the glibc 2.35
|
||||
# loader with the LibTorch 2.8.0 library path (ignoring the inherited
|
||||
# LD_LIBRARY_PATH, so the env's torch 2.14 cannot leak in).
|
||||
# The pipeline selects the shim via KONFAI_ELASTIX_DIR
|
||||
# (src/registration_pipelines.py::_impact_elastix_root).
|
||||
#
|
||||
# Run from the repo root, inside the conda env:
|
||||
# source /opt/conda/etc/profile.d/conda.sh && conda activate longitudinal-313
|
||||
# pip install impact-reg-konfai # the CLI itself
|
||||
# bash scripts/setup_impact_konfai.sh # this script
|
||||
#
|
||||
# First `register` afterwards auto-downloads from HF/GitHub: the preset app
|
||||
# (VBoussot/ImpactReg), the feature models (VBoussot/impact-torchscript-models)
|
||||
# and, on hosts that CAN run the stock binary directly, the binary itself.
|
||||
set -euo pipefail
|
||||
|
||||
KONFAI_CACHE="${KONFAI_CACHE:-$HOME/.cache/konfai}"
|
||||
SHIM="$KONFAI_CACHE/elastix-impact-shim"
|
||||
STOCK="$KONFAI_CACHE/elastix-impact"
|
||||
GLIBC_SRC_DIR="${GLIBC_SRC_DIR:-glibc-2.35}"
|
||||
GLIBC_IMG="${GLIBC_IMG:-niftyreg:2.1.1-ubuntu}"
|
||||
LT_VER="2.8.0"
|
||||
LT_DIR="libtorch-${LT_VER}-cu128"
|
||||
LT_URL="https://download.pytorch.org/libtorch/cu128/libtorch-shared-with-deps-${LT_VER}%2Bcu128.zip"
|
||||
|
||||
echo "[1/5] stock elastix-impact binary (package auto-install)"
|
||||
if [ ! -x "$STOCK/bin/elastix" ]; then
|
||||
python - <<'PY'
|
||||
from pathlib import Path
|
||||
from impact_reg_konfai.models.elastix_install import (
|
||||
ELASTIX_CACHE, get_elastix_bin, install_elastix_impact)
|
||||
if not get_elastix_bin(ELASTIX_CACHE).exists():
|
||||
# flavor auto-detected (cu128 on this host); downloads + extracts the zip
|
||||
install_elastix_impact(ELASTIX_CACHE, force_cuda=False, force_cpu=False)
|
||||
PY
|
||||
fi
|
||||
[ -x "$STOCK/bin/elastix" ] || { echo "stock binary missing: $STOCK/bin/elastix"; exit 1; }
|
||||
|
||||
echo "[2/5] glibc 2.35 from $GLIBC_IMG (docker, one-time)"
|
||||
if [ ! -x "$KONFAI_CACHE/$GLIBC_SRC_DIR/ld-linux-x86-64.so.2" ]; then
|
||||
docker run --rm --entrypoint sh -v "$KONFAI_CACHE":/dest "$GLIBC_IMG" -c \
|
||||
"rm -rf /dest/$GLIBC_SRC_DIR && mkdir -p /dest/$GLIBC_SRC_DIR && \
|
||||
cp -a /lib/x86_64-linux-gnu/. /dest/$GLIBC_SRC_DIR/ && \
|
||||
cp -a /usr/lib/x86_64-linux-gnu/libstdc++.so.6* /dest/$GLIBC_SRC_DIR/ 2>/dev/null || true"
|
||||
fi
|
||||
|
||||
echo "[3/5] LibTorch ${LT_VER} cu128 (self-contained)"
|
||||
if [ ! -f "$SHIM/lib/$LT_DIR/libtorch/lib/libc10.so" ]; then
|
||||
mkdir -p "$SHIM/lib"
|
||||
curl -L --fail -o "$KONFAI_CACHE/$LT_DIR.zip" "$LT_URL"
|
||||
unzip -q "$KONFAI_CACHE/$LT_DIR.zip" -d "$SHIM/lib/$LT_DIR"
|
||||
rm "$KONFAI_CACHE/$LT_DIR.zip"
|
||||
fi
|
||||
# the with-deps zip carries the CUDA libs under hashed names; expose the public
|
||||
# soname elastix links directly
|
||||
LT_LIB="$SHIM/lib/$LT_DIR/libtorch/lib"
|
||||
CUDART="$(ls "$LT_LIB"/libcudart-*.so.12 | head -1)"
|
||||
ln -sf "$(basename "$CUDART")" "$LT_LIB/libcudart.so.12"
|
||||
|
||||
echo "[4/5] shim layout + wrapper"
|
||||
mkdir -p "$SHIM/bin"
|
||||
cp "$STOCK/bin/elastix" "$SHIM/bin/elastix-real"
|
||||
[ -d "$STOCK/lib" ] && cp -a "$STOCK/lib/." "$SHIM/lib/"
|
||||
[ -d "$KONFAI_CACHE/$GLIBC_SRC_DIR" ] && cp -a "$KONFAI_CACHE/$GLIBC_SRC_DIR" "$SHIM/lib/$GLIBC_SRC_DIR"
|
||||
cat > "$SHIM/bin/elastix" <<'WRAP'
|
||||
#!/bin/sh
|
||||
# elastix-impact 1.0.0 shim: the host glibc (2.31) is too old for the binary
|
||||
# (built against glibc 2.35) and the binary expects LibTorch 2.8.0, which the
|
||||
# conda env's torch (2.14) does not provide. Run it through the bundled
|
||||
# glibc 2.35 loader with the bundled self-contained LibTorch 2.8.0 (cu128).
|
||||
ROOT="$(cd "$(dirname "$0")/.." && pwd)"
|
||||
LT="$ROOT/lib/libtorch-2.8.0-cu128/libtorch/lib"
|
||||
GLIBC="$ROOT/lib/glibc-2.35"
|
||||
exec env -u LD_LIBRARY_PATH -u LD_PRELOAD \
|
||||
"$GLIBC/ld-linux-x86-64.so.2" \
|
||||
--library-path "$LT:$ROOT/lib:$GLIBC" \
|
||||
"$ROOT/bin/elastix-real" "$@"
|
||||
WRAP
|
||||
chmod +x "$SHIM/bin/elastix"
|
||||
|
||||
echo "[5/5] verify"
|
||||
KONFAI_ELASTIX_DIR="$SHIM" python - <<'PY'
|
||||
import os
|
||||
from pathlib import Path
|
||||
from impact_reg_konfai.models.elastix_install import get_elastix_bin, loader_env, try_elastix
|
||||
import subprocess
|
||||
root = Path(os.environ["KONFAI_ELASTIX_DIR"])
|
||||
try_elastix(root)
|
||||
r = subprocess.run([str(get_elastix_bin(root).resolve()), "--extended-version"],
|
||||
capture_output=True, text=True, env=loader_env(root))
|
||||
assert r.returncode == 0, r.stderr
|
||||
print(r.stdout.splitlines()[0] if r.stdout else "elastix OK")
|
||||
print("elastix-impact shim OK:", root)
|
||||
PY
|
||||
|
||||
echo
|
||||
echo "Done. The impact pipeline picks the shim up automatically"
|
||||
echo "(src/registration_pipelines.py::_impact_elastix_root), e.g.:"
|
||||
echo " python scripts/bench_registration.py --pipelines impact --n-cases 2"
|
||||
152
src/impact_loss.py
Normal file
152
src/impact_loss.py
Normal file
|
|
@ -0,0 +1,152 @@
|
|||
# Vendored from https://github.com/vboussot/ImpactLoss (IMPACT.py)
|
||||
# Copyright (c) 2025 Valentin Boussot. Apache-2.0.
|
||||
#
|
||||
# IMPACT Reg: A Generic Semantic Loss for Multimodal Image Registration
|
||||
# (Boussot et al., arXiv:2503.24121). Modification relative to upstream:
|
||||
# model files are resolved from a local cache dir first (``model_cache`` /
|
||||
# the ``IMPACT_MODELS_DIR`` env var) before falling back to the Hugging
|
||||
# Face download, so offline hosts can use the already-cached
|
||||
# ``data/impact_models`` layout.
|
||||
|
||||
import os
|
||||
|
||||
import torch
|
||||
from huggingface_hub import hf_hub_download
|
||||
|
||||
IMPACT_HF_REPO = "VBoussot/impact-torchscript-models"
|
||||
|
||||
|
||||
def _resolve_model(model_name, model_cache):
|
||||
"""Local cache path if present, else the Hugging Face download."""
|
||||
if model_cache:
|
||||
p = os.path.join(model_cache, model_name)
|
||||
if os.path.exists(p):
|
||||
return p
|
||||
return hf_hub_download(repo_id=IMPACT_HF_REPO, filename=model_name,
|
||||
repo_type="model", revision=None) # nosec B615
|
||||
|
||||
|
||||
class IMPACTReg(torch.nn.Module):
|
||||
"""
|
||||
IMPACT-Reg Loss Module
|
||||
------------------
|
||||
A similarity metric based on pretrained TorchScript feature extractors.
|
||||
Computes a weighted L1 distance between feature maps of two images.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
model_name : str
|
||||
Name of the TorchScript model (e.g. "TS/M730").
|
||||
shape : list[int]
|
||||
Expected spatial size. Use [0, 0, 0] for no resample.
|
||||
in_channels : int
|
||||
Number of input channels (before adapting to the model).
|
||||
weights : list[float]
|
||||
Per-layer feature weights.
|
||||
model_cache : str, optional
|
||||
Directory the model files are looked up in before downloading.
|
||||
"""
|
||||
def __init__(
|
||||
self, model_name: str, shape: list[int] = [0, 0], in_channels: int = 1,
|
||||
weights: list[float] = [1], model_cache: str = None,
|
||||
) -> None:
|
||||
super().__init__()
|
||||
if model_name is None:
|
||||
return
|
||||
self.in_channels = in_channels
|
||||
self.loss = torch.nn.L1Loss()
|
||||
self.weights = weights
|
||||
self.nb_layer = len(weights)
|
||||
|
||||
# Load the TorchScript model (local cache first, HF download after)
|
||||
model_cache = model_cache or os.environ.get("IMPACT_MODELS_DIR", "")
|
||||
self.model_path = _resolve_model(model_name, model_cache)
|
||||
|
||||
self.model: torch.nn.Module = torch.jit.load(self.model_path, map_location=torch.device("cpu")) # nosec B614
|
||||
self.dim = len(shape)
|
||||
self.shape = shape if all(s > 0 for s in shape) else None
|
||||
|
||||
dummy_input = torch.zeros((1, self.in_channels, *(self.shape if self.shape else [128] * self.dim))).to(0)
|
||||
try:
|
||||
out = self.model.to(0)(dummy_input, torch.tensor([self.nb_layer]))
|
||||
if not isinstance(out, (list, tuple)):
|
||||
raise TypeError(f"Expected model output to be a list or tuple, but got {type(out)}.")
|
||||
if self.nb_layer != len(out):
|
||||
raise ValueError(
|
||||
f"Loss 'L1': mismatch between the number of weights "
|
||||
f"({self.nb_layer}) and the number of model outputs "
|
||||
f"({len(out)}). Each output must have a corresponding weight."
|
||||
)
|
||||
except Exception as e:
|
||||
msg = (
|
||||
f"[Model Sanity Check Failed]\n"
|
||||
f"Input shape attempted: {dummy_input.shape}\n"
|
||||
f"Error: {type(e).__name__}: {e}"
|
||||
)
|
||||
raise RuntimeError(msg) from e
|
||||
self.model = None
|
||||
|
||||
def preprocessing(self, tensor: torch.Tensor) -> torch.Tensor:
|
||||
"""
|
||||
Resize and adjust the tensor channels to match model expectations.
|
||||
|
||||
Note:
|
||||
This method does not perform any intensity normalization.
|
||||
Input images must be provided in their raw (non-normalized) form,
|
||||
i.e., in their original intensity space (e.g., HU for CT, native
|
||||
values for MRI). Intensity normalization / standardization is the
|
||||
responsibility of the IMPACT models.
|
||||
"""
|
||||
if self.shape is not None and not all(
|
||||
tensor.shape[-i - 1] == size for i, size in enumerate(reversed(self.shape[2:]))
|
||||
):
|
||||
if tensor.dtype == torch.uint8:
|
||||
mode = "nearest"
|
||||
elif len(tensor.shape) < 4:
|
||||
mode = "bilinear"
|
||||
else:
|
||||
mode = "trilinear"
|
||||
tensor = torch.nn.functional.interpolate(
|
||||
tensor, mode=mode, size=tuple(self.shape), align_corners=False
|
||||
).type(torch.float32)
|
||||
|
||||
# Replicate channels if needed (e.g., 1 -> 3)
|
||||
if tensor.shape[1] != self.in_channels:
|
||||
tensor = tensor.repeat(tuple([1, self.in_channels] + [1 for _ in range(self.dim)]))
|
||||
return [tensor, torch.tensor([self.nb_layer]),
|
||||
torch.tensor([tensor.min(), tensor.max(), tensor.mean(), tensor.std()])]
|
||||
|
||||
def _compute(self, output: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
|
||||
"""Compute the feature-based L1 loss between two tensors."""
|
||||
loss = torch.zeros(1, device=output.device, dtype=torch.float32)
|
||||
|
||||
output = self.preprocessing(output)
|
||||
target = self.preprocessing(target)
|
||||
|
||||
for weight, output_features, target_features in zip(
|
||||
self.weights, self.model(*output), self.model(*target)
|
||||
):
|
||||
if weight == 0:
|
||||
continue
|
||||
loss = loss + weight * self.loss(output_features, target_features)
|
||||
return loss
|
||||
|
||||
def forward(self, output: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
|
||||
"""
|
||||
Main forward method.
|
||||
If the model is 2D and inputs are 3D, compute slice-wise average loss.
|
||||
"""
|
||||
if self.model is None:
|
||||
self.model = torch.jit.load(self.model_path) # nosec B614
|
||||
self.model.to(output.device)
|
||||
self.model.eval()
|
||||
|
||||
loss = torch.zeros(1, device=output.device, dtype=torch.float32)
|
||||
# 3D -> 2D model case: iterate over slices
|
||||
if len(output.shape) == 5 and self.dim == 2:
|
||||
for i in range(output.shape[2]):
|
||||
loss = loss + self._compute(output[:, :, i, ...], target[:, :, i, ...])
|
||||
loss = loss / output.shape[2]
|
||||
else:
|
||||
loss = self._compute(output, target)
|
||||
return loss
|
||||
|
|
@ -31,8 +31,6 @@ Shared rules for every pipeline:
|
|||
- **The T1c (MR) is always the fixed image** (moving = CT). When both inputs
|
||||
are the same class (both CT, or a clipped CT that no longer reads as CT)
|
||||
the T1c-slot (second) argument is kept as fixed; see :func:`fixed_moving`.
|
||||
Every pipeline -- including ``impact``, whose ImpactReg MR_CT convention
|
||||
coincides with this rule -- registers fixed=T1c / moving=CT.
|
||||
- **CT brain-window clip for the pipelines that benefit from it**: per the
|
||||
CT-clipping A/B (see REGISTRATION_BENCHMARK.md), ``niftyreg`` and
|
||||
``synthmorph`` clip the CT (the moving image) to [0, 80] HU before
|
||||
|
|
@ -53,18 +51,13 @@ default center-of-mass alignment, simpleitk starts from identity, niftyreg
|
|||
- ``register_elastix`` : ITK-Elastix Python API (pip ``itk`` +
|
||||
``itk-elastix``, elastix 5.x: ``EulerTransform`` rigid + Mattes MI, ASGD).
|
||||
``AutomaticTransformInitialization`` (CenterOfGravity) start.
|
||||
- ``register_impact`` : IMPACT (semantic-similarity metric) via the
|
||||
``impact-reg-konfai`` package (pip; the KonfAI runtime drives the
|
||||
elastix-impact 5.2 binary -- no Docker). Default preset
|
||||
``Elastix_IMPACT_Static``: the published successor of the ImpactReg
|
||||
``MR_CT_TS`` reference map (elastix B-spline + per-level MIND +
|
||||
TotalSegmentator M730, Static mode). Registered fixed=T1c/moving=CT (the
|
||||
ImpactReg MR_CT convention) so the preset's output field (on the fixed
|
||||
grid) is directly the T1c->CT contract transform; CenterOfGravity auto
|
||||
init + the 0.1 sample-validity gate as raw-elastix overrides. GPU.
|
||||
Full volumes, no mask (shared rules above). First use auto-downloads the
|
||||
preset app, models and binary into ``~/.cache``; on hosts too old for the
|
||||
stock binary, ``scripts/setup_impact_konfai.sh`` builds the shim.
|
||||
- ``register_impact`` : IMPACT (semantic-similarity metric) inside the
|
||||
``vboussot/elastix_impact`` Docker image (Elastix 5.2 + TorchScript feature
|
||||
models as a native Elastix metric). Rigid 6-DOF ``EulerTransform`` driven
|
||||
by the MIND + TotalSegmentator static feature combination of the ImpactReg
|
||||
``MR_CT_TS`` reference map (per-level models / scales / layer weights) + a
|
||||
Mattes-MI secondary metric (w 0.4). GPU. Full volumes, no mask (shared
|
||||
rules above). Needs Docker + the image + the model assets under the cache.
|
||||
- ``register_niftyreg`` : NiftyReg (KCL-BMEIS suite) ``reg_aladin`` --
|
||||
symmetric block-matching + trimmed least squares (Modat et al., J. Med.
|
||||
Img. 2014), NCC-based block similarity (cross-modality tolerant). Rigid
|
||||
|
|
@ -93,10 +86,8 @@ per toolkit. :func:`fix_orientation` resolves this by warping the CT with both
|
|||
``T`` and ``T^-1`` and keeping whichever gives the smaller skull-ring
|
||||
concentricity gap (a pure correctness gate -- a wrongly-oriented rigid transform
|
||||
lands the head on the far side of the box and yields a pathological gap). It is
|
||||
applied to elastix / antspyx / synthmorph, not to the trusted production
|
||||
SimpleITK path. ``impact`` returns a displacement field (which has no
|
||||
``GetInverse``), so it uses :func:`_orient_impact` -- the same skull-ring
|
||||
decision against a sign-flipped (first-order reverse) proxy field.
|
||||
applied to elastix / antspyx / synthmorph / impact, not to the trusted
|
||||
production SimpleITK path.
|
||||
"""
|
||||
import os
|
||||
import re
|
||||
|
|
@ -670,91 +661,46 @@ def register_synthmorph(ct, t1c, model="rigid", gpu=None, threads=None,
|
|||
|
||||
|
||||
# --------------------------------------------------------------------------- #
|
||||
# 5. IMPACT (impact-reg-konfai -- KonfAI runtime, no Docker)
|
||||
# 5. IMPACT (Docker image vboussot/elastix_impact)
|
||||
# --------------------------------------------------------------------------- #
|
||||
|
||||
IMPACT_CLI = "impact-reg-konfai"
|
||||
# default preset from the published ImpactReg database (VBoussot/ImpactReg,
|
||||
# resolved by the CLI): elastix B-spline driven by the IMPACT semantic metric
|
||||
# -- per-level MIND (L1) + TotalSegmentator M730 (deepest layer, Dice), Static
|
||||
# mode (whole-image features, no backprop), 4 scales, 400/300/200/200
|
||||
# iterations, 2000 spatial samples, + Mattes MI (w 0.4) + bending energy.
|
||||
# The published successor of the old MR_CT_TS reference parameter map.
|
||||
IMPACT_DEFAULT_PRESET = "Elastix_IMPACT_Static"
|
||||
# M6 robustness overrides -- raw elastix parameter lines the package applies
|
||||
# on top of the preset's generated map (the ``parameter_overrides`` knob):
|
||||
# * CenterOfGravity auto-init (instead of the preset's GeometricalCenter):
|
||||
# aligns the intensity centroids -- the T1c head-crop and the CT head+neck
|
||||
# FOV have their buffer centres ~50-100 mm apart, and the GeometricalCenter
|
||||
# start leaves the metric with almost nothing in-buffer;
|
||||
# * RequiredRatioOfValidSamples 0.1 (build default 0.25): the FOV-mismatched
|
||||
# pairs keep only a minority of the random spatial samples in-buffer at the
|
||||
# init; 0.1 keeps the divergence gate while letting the runs proceed (a
|
||||
# genuinely diverging transform, <10% in-buffer, still aborts).
|
||||
IMPACT_PARAM_OVERRIDES = (
|
||||
"AutomaticTransformInitialization=true",
|
||||
"AutomaticTransformInitializationMethod=CenterOfGravity",
|
||||
"RequiredRatioOfValidSamples=0.1",
|
||||
IMPACT_IMAGE = "vboussot/elastix_impact"
|
||||
IMPACT_ELASTIX_BIN = "/lib/elastix-install/bin/elastix"
|
||||
IMPACT_MODEL_CACHE = "data/impact_models"
|
||||
IMPACT_HF_MODEL_URL = (
|
||||
"https://huggingface.co/VBoussot/impact-torchscript-models/resolve/main/{rel}"
|
||||
)
|
||||
# The stock elastix-impact 1.0.0 binary (vboussot/ImpactElastix) needs
|
||||
# glibc >= 2.35 and links LibTorch 2.8.0; a host that cannot run it directly
|
||||
# (this one: glibc 2.31 + env torch 2.14) builds a self-contained shim with
|
||||
# scripts/setup_impact_konfai.sh, selected via KONFAI_ELASTIX_DIR.
|
||||
IMPACT_ELASTIX_SHIM = os.path.join(
|
||||
os.path.expanduser("~"), ".cache", "konfai", "elastix-impact-shim")
|
||||
IMPACT_ELASTIX_STOCK = os.path.join(
|
||||
os.path.expanduser("~"), ".cache", "konfai", "elastix-impact")
|
||||
# orientation-gate threshold: a candidate DVF is kept as-is when its
|
||||
# skull-ring gap score (std_mm + mid_mm) is below this. A misdirected field
|
||||
# lands the head on the far side of the box (gap ~100-300 mm), so the margin
|
||||
# is wide; see _orient_impact.
|
||||
IMPACT_GAP_ACCEPT = 60.0
|
||||
# The handcrafted MIND descriptor (whole-image features, layer mask "1", L1)
|
||||
# combined per level with a TotalSegmentator feature model (deepest layers,
|
||||
# Dice) -- the MIND+TS static combination of the ImpactReg ``MR_CT_TS``
|
||||
# reference parameter map, coarse -> fine. Per level: (TS model, voxel-size
|
||||
# scale in mm, MIND layer mask, TS layer mask, MIND weight, TS weight).
|
||||
IMPACT_MIND_MODEL = "MIND/R1D2_3D.pt"
|
||||
IMPACT_LEVELS = (
|
||||
("TS/M852.pt", 6.0, "1", "0000001", 0.2, 0.8),
|
||||
("TS/M852.pt", 3.0, "1", "0000001", 0.3, 0.7),
|
||||
("TS/M850.pt", 2.0, "1", "00000001", 0.6, 0.4),
|
||||
("TS/M850.pt", 2.0, "1", "00000001", 0.7, 0.3),
|
||||
)
|
||||
IMPACT_DEFAULT_ITERS = (400, 300, 200, 200)
|
||||
IMPACT_DEFAULT_SAMPLES = 2000
|
||||
# secondary Mattes-MI metric weight (the semantic metric gets weight 1.0)
|
||||
IMPACT_MI_WEIGHT = 0.4
|
||||
|
||||
|
||||
def _impact_cli_ok():
|
||||
"""Return (ok, note): is the impact-reg-konfai CLI installed?"""
|
||||
if shutil.which(IMPACT_CLI) is None:
|
||||
return False, f"{IMPACT_CLI} not on PATH (pip install impact-reg-konfai)"
|
||||
def _impact_docker_ok():
|
||||
"""Return (ok, note): is the Docker CLI + the IMPACT image available?"""
|
||||
if shutil.which("docker") is None:
|
||||
return False, "docker CLI not found on PATH"
|
||||
try:
|
||||
import importlib.metadata
|
||||
ver = importlib.metadata.version("impact-reg-konfai")
|
||||
except Exception: # noqa
|
||||
ver = "?"
|
||||
return True, f"{IMPACT_CLI} {ver}"
|
||||
|
||||
|
||||
_ELASTIX_ROOT_CACHE = None
|
||||
|
||||
|
||||
def _impact_elastix_root():
|
||||
"""The elastix-impact install root that runs on this host, or None.
|
||||
|
||||
Tried in order: a caller-set ``KONFAI_ELASTIX_DIR``, the setup-script shim
|
||||
(glibc 2.35 loader + LibTorch 2.8.0, for hosts too old for the stock
|
||||
binary), and the package's own auto-install cache (populated by the first
|
||||
run when the host can run the stock binary directly). Each candidate is
|
||||
actually executed (``elastix -h``) so a stale/broken install is skipped."""
|
||||
global _ELASTIX_ROOT_CACHE
|
||||
if _ELASTIX_ROOT_CACHE is not None:
|
||||
return _ELASTIX_ROOT_CACHE
|
||||
from pathlib import Path
|
||||
from impact_reg_konfai.models import elastix_install as _ei
|
||||
|
||||
root = os.environ.get("KONFAI_ELASTIX_DIR", "")
|
||||
candidates = [c for c in (root, IMPACT_ELASTIX_SHIM, IMPACT_ELASTIX_STOCK)
|
||||
if c]
|
||||
found = None
|
||||
for c in candidates:
|
||||
if not os.path.exists(os.path.join(c, "bin", "elastix")):
|
||||
continue
|
||||
try:
|
||||
_ei.try_elastix(Path(c))
|
||||
found = c
|
||||
break
|
||||
except Exception: # noqa
|
||||
continue
|
||||
_ELASTIX_ROOT_CACHE = found
|
||||
return found
|
||||
r = subprocess.run(["docker", "image", "inspect", IMPACT_IMAGE],
|
||||
capture_output=True, text=True, timeout=30)
|
||||
if r.returncode != 0:
|
||||
return False, (f"docker image {IMPACT_IMAGE} not pulled "
|
||||
f"(run: docker pull {IMPACT_IMAGE})")
|
||||
except Exception as e: # noqa
|
||||
return False, f"docker image check failed ({type(e).__name__}: {e})"
|
||||
return True, IMPACT_IMAGE
|
||||
|
||||
|
||||
_NGPUS_CACHE = None
|
||||
|
|
@ -773,156 +719,196 @@ def _n_gpus():
|
|||
return _NGPUS_CACHE
|
||||
|
||||
|
||||
def _impact_dvf_from_h5(path):
|
||||
"""An ITK-form HDF5 transform (the ``:itktransform`` form the KonfAI
|
||||
presets write) read with h5py -- the SimpleITK build in this env has no
|
||||
ITK HDF5 transform reader. Returns ``(DisplacementFieldTransform,
|
||||
dvf_image)``; the field is in mm, channel-last ``(z, y, x, 3)``."""
|
||||
import h5py
|
||||
with h5py.File(str(path), "r") as f:
|
||||
grp = f["TransformGroup/0"]
|
||||
ttype = grp["TransformType"][()][0]
|
||||
if isinstance(ttype, bytes):
|
||||
ttype = ttype.decode()
|
||||
fixed = grp["TransformFixedParameters"][()]
|
||||
params = grp["TransformParameters"][()]
|
||||
if not str(ttype).startswith("DisplacementFieldTransform"):
|
||||
raise RuntimeError(f"impact: unsupported h5 transform type {ttype!r}")
|
||||
size = fixed[:3][::-1].astype(int) # stored (x, y, z) -> array (z, y, x)
|
||||
arr = params.reshape(int(size[0]), int(size[1]), int(size[2]), 3)
|
||||
img = sitk.GetImageFromArray(arr)
|
||||
img.SetOrigin(tuple(float(v) for v in fixed[3:6]))
|
||||
img.SetSpacing(tuple(float(v) for v in fixed[6:9]))
|
||||
img.SetDirection(tuple(float(v) for v in fixed[9:18]))
|
||||
# this SimpleITK build invalidates the image wrapper handed to the DVF
|
||||
# constructor (it comes back as an empty uint8 image), so build the
|
||||
# transform from a copy and keep ``img`` for the caller
|
||||
return sitk.DisplacementFieldTransform(sitk.Image(img)), img
|
||||
def _impact_models_host():
|
||||
"""Resolve the IMPACT feature models to files under the model cache, or
|
||||
raise :class:`PipelineUnavailable` with the exact download commands for
|
||||
the missing ones. Returns the de-duplicated paths relative to the cache
|
||||
(which the pipeline bind-mounts at ``/Data/Models`` inside the
|
||||
container)."""
|
||||
cache = d(IMPACT_MODEL_CACHE)
|
||||
rels = [IMPACT_MIND_MODEL] + [rel for rel, *_ in IMPACT_LEVELS]
|
||||
missing = [r for r in dict.fromkeys(rels)
|
||||
if not os.path.exists(os.path.join(cache, r))]
|
||||
if missing:
|
||||
cmds = "\n".join(
|
||||
f" mkdir -p {os.path.join(cache, os.path.dirname(r))} && "
|
||||
f"curl -L -o {os.path.join(cache, r)} {IMPACT_HF_MODEL_URL.format(rel=r)}"
|
||||
for r in missing)
|
||||
raise PipelineUnavailable(
|
||||
f"impact: model(s) not found under {cache}.\nDownload once with:\n"
|
||||
f"{cmds}\n(or `sh Data/Models/download_models.sh` from the "
|
||||
f"ImpactLoss repo).")
|
||||
return list(dict.fromkeys(rels))
|
||||
|
||||
|
||||
def _impact_read_transform(path):
|
||||
"""The preset's single-case transform output as
|
||||
``(sitk.Transform, dvf_image_or_None)``.
|
||||
|
||||
KonfAI presets write the registration in whatever form the preset
|
||||
declares: an ITK-form HDF5 DisplacementFieldTransform (``Transform.h5``)
|
||||
by default, or an ITK transform file (``.itk.txt`` / ``.tfm``) or a vector
|
||||
image (``.mha``) for other presets."""
|
||||
from pathlib import Path
|
||||
p = Path(path)
|
||||
if p.name.endswith(".itk.txt") or p.suffix in (".h5", ".tfm"):
|
||||
if p.suffix == ".h5":
|
||||
return _impact_dvf_from_h5(p)
|
||||
return sitk.ReadTransform(str(p)), None
|
||||
img = sitk.ReadImage(str(p))
|
||||
return (sitk.DisplacementFieldTransform(
|
||||
sitk.Cast(img, sitk.sitkVectorFloat64)), img)
|
||||
|
||||
|
||||
def _orient_impact(ct, t1c, cand, dvf_img):
|
||||
"""``(T, inverted)`` in the contract direction, for an impact candidate.
|
||||
|
||||
``cand`` is nominally T1c->CT: the preset ran with fixed=T1c / moving=CT
|
||||
and its output field is on the fixed (T1c) grid. Displacement fields
|
||||
cannot be inverted with ``GetInverse`` (ITK), so the direction gate does
|
||||
not compare ``cand`` with ``cand^-1`` (see :func:`fix_orientation`); it
|
||||
compares the skull-ring gap of ``cand`` against a reverse proxy -- the
|
||||
sign-flipped field (first-order inverse, exact enough for the decision:
|
||||
a misdirected DVF lands the head on the far side of the box, gap
|
||||
~100+ mm instead of tens of mm) -- and falls back to it only when the
|
||||
nominal direction is clearly wrong."""
|
||||
ha = sitk.GetArrayFromImage(t1c).astype("float32")
|
||||
s0 = _gap_score(_skull_gap(_warp_ct(ct, t1c, cand), ha))
|
||||
if s0 <= IMPACT_GAP_ACCEPT:
|
||||
return cand, False
|
||||
if dvf_img is not None:
|
||||
img = sitk.GetImageFromArray(-sitk.GetArrayViewFromImage(dvf_img))
|
||||
img.CopyInformation(dvf_img)
|
||||
rev = sitk.DisplacementFieldTransform(img)
|
||||
def _impact_param_map(gpu, iters, samples, voxel_sizes):
|
||||
"""Build the Elastix-5.2 parameter map for a RIGID (Euler) IMPACT
|
||||
registration: the MIND + TotalSegmentator static feature combination of
|
||||
the ImpactReg ``MR_CT_TS`` reference map (one model pair per level,
|
||||
coarse -> fine, see :data:`IMPACT_LEVELS`) + a secondary Mattes-MI metric
|
||||
(:data:`IMPACT_MI_WEIGHT`). The image pyramid is NOT resampled (all-1
|
||||
schedules); the scale comes from the per-level ImpactVoxelSize (as the
|
||||
IMPACT docs recommend). ``iters`` is a per-level tuple or a single int.
|
||||
"""
|
||||
n = len(voxel_sizes)
|
||||
if isinstance(iters, (int,)):
|
||||
iters_l = str(iters)
|
||||
else:
|
||||
try:
|
||||
rev = cand.GetInverse()
|
||||
except Exception: # noqa: parametric transform with no inverse
|
||||
return cand, False
|
||||
s1 = _gap_score(_skull_gap(_warp_ct(ct, t1c, rev), ha))
|
||||
if s1 < s0:
|
||||
return rev, True
|
||||
return cand, False
|
||||
iters_l = " ".join(f"{i:g}" for i in iters)
|
||||
mixed = "true" if gpu >= 0 else "false"
|
||||
L = ["// IMPACT semantic-similarity metric (vboussot/ImpactLoss),"]
|
||||
L.append("// MIND + TotalSegmentator static combination of the")
|
||||
L.append("// VBoussot/ImpactReg MR_CT_TS reference map (EulerTransform")
|
||||
L.append("// instead of the reference RecursiveBSpline -- rigid)")
|
||||
L.append('(Metric "Impact" "AdvancedMattesMutualInformation")')
|
||||
L.append("(Metric0Weight 1)")
|
||||
L.append(f"(Metric1Weight {IMPACT_MI_WEIGHT})")
|
||||
L.append('(UseRelativeWeights "false")')
|
||||
for i, (ts_rel, _vs, mind_mask, ts_mask, mind_w, ts_w) in enumerate(IMPACT_LEVELS):
|
||||
vs = voxel_sizes[i]
|
||||
L += [
|
||||
f'(ImpactModelsPath{i} "/Data/Models/{IMPACT_MIND_MODEL}" '
|
||||
f'"/Data/Models/{ts_rel}")',
|
||||
f"(ImpactDimension{i} 3 3)",
|
||||
f"(ImpactNumberOfChannels{i} 1 1)",
|
||||
f"(ImpactPatchSize{i} 0 0 0 0 0 0)",
|
||||
f"(ImpactVoxelSize{i} {vs:g} {vs:g} {vs:g} {vs:g} {vs:g} {vs:g})",
|
||||
f'(ImpactLayersMask{i} "{mind_mask}" "{ts_mask}")',
|
||||
f"(ImpactSubsetFeatures{i} 32 64)",
|
||||
f"(ImpactPCA{i} 0 0)",
|
||||
f'(ImpactDistance{i} "L1" "Dice")',
|
||||
f"(ImpactLayersWeight{i} {mind_w} {ts_w})",
|
||||
]
|
||||
L += [
|
||||
'(ImpactMode "Static")',
|
||||
f"(ImpactGPU {gpu})",
|
||||
f'(ImpactUseMixedPrecision "{mixed}")',
|
||||
"(ImpactFeaturesMapUpdateInterval -1)",
|
||||
'(ImpactWriteFeatureMaps "false")',
|
||||
# safety gate: abort if < ratio of the spatial samples map inside the
|
||||
# moving buffer. With fixed=T1c / moving=CT the samples (drawn from
|
||||
# the tight T1c head crop) map mostly inside the large CT head-neck
|
||||
# FOV, so the gate rarely fires; 0.1 (vs the build default 0.25)
|
||||
# keeps a divergence gate for the FOV mismatch.
|
||||
'(CheckNumberOfSamples "true")',
|
||||
"(RequiredRatioOfValidSamples 0.1)",
|
||||
"",
|
||||
"// rigid 6-DOF to be optimized (auto center init, no -t0)",
|
||||
'(Transform "EulerTransform")',
|
||||
'(TransformBase "ElastixTransformBase")',
|
||||
'(TransformParameterReadoutFormat "AllParametersOneLine")',
|
||||
'(FixedCenteredTransform "true")',
|
||||
'(HowToCombineTransforms "Compose")',
|
||||
'(AutomaticTransformInitialization "true")',
|
||||
# align intensity centroids (heads), not buffer corners -- the T1c
|
||||
# head crop and the CT head+neck FOV have their buffer centres far
|
||||
# apart, so GeometricalCenter leaves the heads ~100 mm apart and the
|
||||
# metric aborts ("too many samples outside buffer")
|
||||
'(AutomaticTransformInitializationMethod "CenterOfGravity")',
|
||||
'(AutomaticScalesEstimation "true")',
|
||||
"",
|
||||
"(ImageDimension 3)",
|
||||
'(FixedInternalImagePixelType "float")',
|
||||
'(MovingInternalImagePixelType "float")',
|
||||
'(UseDirectionCosines "true")',
|
||||
"",
|
||||
'(Registration "MultiMetricMultiResolutionRegistration")',
|
||||
f"(NumberOfResolutions {n})",
|
||||
f"(MaximumNumberOfIterations {iters_l})",
|
||||
f"(NumberOfSpatialSamples {samples})",
|
||||
"",
|
||||
'(FixedImagePyramid "FixedGenericImagePyramid")',
|
||||
'(MovingImagePyramid "MovingGenericImagePyramid")',
|
||||
f"(FixedImagePyramidRescaleSchedule {' '.join('1' for _ in range(3 * n))})",
|
||||
f"(MovingImagePyramidRescaleSchedule {' '.join('1' for _ in range(3 * n))})",
|
||||
"",
|
||||
'(Optimizer "AdaptiveStochasticGradientDescent")',
|
||||
"(MaximumNumberOfSamplingAttempts 8)",
|
||||
'(UseAdaptiveStepSizes "true")',
|
||||
'(UseMultiThreadingForMetrics "true")',
|
||||
'(ASGDParameterEstimationMethod "DisplacementDistribution")',
|
||||
"(SigmoidInitialTime 0.0)",
|
||||
'(NoiseCompensation "true")',
|
||||
"(NumberOfSamplesForExactGradient 4096)",
|
||||
'(AutomaticParameterEstimation "true")',
|
||||
"",
|
||||
'(ImageSampler "RandomCoordinate")',
|
||||
'(NewSamplesEveryIteration "true")',
|
||||
'(Interpolator "BSplineInterpolator")',
|
||||
"(BSplineInterpolationOrder 3)",
|
||||
'(UseRandomSampleRegion "false")',
|
||||
"",
|
||||
'(WriteResultImage "false")',
|
||||
"(DefaultPixelValue -1024)",
|
||||
'(ResultImageFormat "mha")',
|
||||
'(ITKTransformOutputFileNameExtension "itk.txt")',
|
||||
'(WriteITKCompositeTransform "true")',
|
||||
'(UseBinaryFormatForTransformationParameters "false")',
|
||||
'(WriteTransformParametersEachIteration "false")',
|
||||
'(WriteTransformParametersEachResolution "false")',
|
||||
]
|
||||
return "\n".join(L) + "\n"
|
||||
|
||||
|
||||
def register_impact(ct, t1c, preset=None, gpu=None, iters=None, samples=None,
|
||||
def register_impact(ct, t1c, gpu=None, iters=IMPACT_DEFAULT_ITERS,
|
||||
samples=IMPACT_DEFAULT_SAMPLES, threads=12,
|
||||
keep_scratch=False):
|
||||
"""IMPACT (semantic-similarity metric) CT->T1c via the
|
||||
``impact-reg-konfai`` package (pip; KonfAI runtime driving the
|
||||
elastix-impact 5.2 binary; no Docker).
|
||||
"""IMPACT rigid CT->T1c (fixed=T1c, moving=CT) in the Docker image
|
||||
``vboussot/elastix_impact`` (Elastix 5.2.0 + IMPACT semantic metric).
|
||||
|
||||
IMPACT (arXiv:2503.24121) replaces the intensity metric with a *semantic*
|
||||
one. The default preset ``Elastix_IMPACT_Static`` is the published
|
||||
successor of the old ImpactReg ``MR_CT_TS`` reference parameter map: an
|
||||
elastix **B-spline** (4 scales, FinalGridSpacing 14 mm, 400/300/200/200
|
||||
iterations, 2000 spatial samples, bending-energy regularizer) driven by
|
||||
the IMPACT metric -- a per-level **MIND** (L1) + **TotalSegmentator M730**
|
||||
(deepest layer, Dice) combination in **Static** mode (whole-image
|
||||
features, no backprop), plus a Mattes-MI secondary metric (w 0.4). Other
|
||||
presets (``Elastix_IMPACT_Jacobian``, ``Generic_Rigid``,
|
||||
``Generic_Rigid_BSpline``, ``FireANTs_*``, ``ConvexAdam_*``) are
|
||||
selectable with ``preset=``; the live database is listed by
|
||||
``impact-reg-konfai register --help``.
|
||||
one: pretrained TorchScript feature models are run on the images and the
|
||||
registration minimizes the distance between the extracted feature maps.
|
||||
The metric here is the MIND + TotalSegmentator **static** combination of
|
||||
the ImpactReg ``MR_CT_TS`` reference map (``MR_CT_TS/ParameterMap_MRI_TS``
|
||||
in ``VBoussot/ImpactReg``): per level the MIND handcrafted descriptor
|
||||
(``MIND/R1D2_3D.pt``, L1) is combined with a TotalSegmentator deepest-layer
|
||||
feature (``TS/M852.pt`` coarse, ``TS/M850.pt`` fine, Dice), layer weights
|
||||
0.2/0.8 -> 0.7/0.3, at voxel sizes 6/3/2/2 mm (scales with the moving
|
||||
image's spacing), plus a secondary Mattes-MI metric (weight 0.4). The
|
||||
models standardize intensity inside the metric (zero mean / unit variance
|
||||
per the model card), so no pipeline-side pre-normalization is applied --
|
||||
the volumes go in in their raw intensity space. Intensity standardization
|
||||
is affine-invariant, so pre-normalizing would be an exact no-op anyway.
|
||||
|
||||
Fixed/moving: the pair is registered in the ImpactReg **MR_CT**
|
||||
convention (fixed=T1c, moving=CT), which coincides with the shared
|
||||
fixed-image rule (the T1c is the fixed image). The preset's output is a
|
||||
displacement field on the **fixed** grid mapping fixed->moving, so the
|
||||
field comes out directly in the contract direction T1c->CT (displacement
|
||||
fields cannot be inverted with ``GetInverse``, which the fixed=CT
|
||||
direction would have required). That also keeps the metric's
|
||||
sample-validity gate healthy: samples drawn from the tight T1c head crop
|
||||
(fixed) map mostly inside the large CT head+neck FOV (moving), unlike
|
||||
fixed=CT where only ~14% of the samples are in-buffer at the auto-init.
|
||||
The transform is the preset's native deformable B-spline (a DVF) -- the
|
||||
contract (warp the CT concentric with the MR head) is unchanged.
|
||||
The optimized transform is a rigid 6-DOF ``EulerTransform`` (the 5.x name
|
||||
for the classic 3D rigid) with ASGD -- no image resampling pyramid, the
|
||||
scale comes from the per-level ImpactVoxelSize. No manual centroid init:
|
||||
elastix's ``AutomaticTransformInitialization`` (CenterOfGravity) aligns
|
||||
the intensity centroids.
|
||||
|
||||
M6 robustness overrides (raw elastix lines the package applies on top of
|
||||
the preset's generated map): CenterOfGravity auto init (the preset's
|
||||
GeometricalCenter start leaves the intensity centroids ~50-100 mm apart
|
||||
on the head+neck FOV mismatch) and ``RequiredRatioOfValidSamples 0.1``
|
||||
(the build default 0.25 aborts the FOV-mismatched pairs at the init; 0.1
|
||||
keeps the divergence gate).
|
||||
Like every other pipeline this runs on the **full volumes with
|
||||
fixed=T1c / moving=CT** (the shared fixed-image rule; no mask, no crop /
|
||||
pad). The metric draws its random spatial samples from the fixed (T1c)
|
||||
buffer and evaluates them where they map inside the moving (CT) buffer;
|
||||
the samples of the tight T1c head crop map mostly inside the large CT
|
||||
head-neck FOV, so the 0.1 sample-validity gate rarely fires. The
|
||||
returned transform satisfies the unified contract (T1c->CT);
|
||||
:func:`fix_orientation` verifies the direction against the skull ring.
|
||||
|
||||
The package auto-downloads its one-time assets from HF/GitHub on first
|
||||
use: the preset app (``VBoussot/ImpactReg``), the TorchScript feature
|
||||
models (``VBoussot/impact-torchscript-models``) and the elastix-impact
|
||||
binary (``vboussot/ImpactElastix``) into ``~/.cache``. The binary needs
|
||||
glibc >= 2.35 and LibTorch 2.8.0; on a host that cannot run it directly
|
||||
(this one: glibc 2.31 + torch 2.14) ``scripts/setup_impact_konfai.sh``
|
||||
builds a self-contained shim, selected via ``KONFAI_ELASTIX_DIR``.
|
||||
Budget: the default ``iters=(400, 300, 200, 200)`` per level /
|
||||
``samples=2000`` is the reference map's budget (~75-80 s/case on the GPU).
|
||||
Pass a single int to ``iters`` to use one budget for every level. A GPU
|
||||
is used when one is visible (auto-spread over the host GPUs by pid);
|
||||
pass ``gpu=-1`` to force CPU (mixed precision is then turned off).
|
||||
The result is read back from ``TransformParameters.0.itk.txt`` (an
|
||||
``Euler3DTransform`` in the world physical frame).
|
||||
|
||||
Budget: ``iters`` / ``samples`` forward to the preset's
|
||||
``max_iterations`` / ``spatial_samples`` knobs (only effective for
|
||||
presets without a per-resolution model matrix; the default preset's
|
||||
matrix wins). A GPU is used when one is visible (auto-spread over the
|
||||
host GPUs by pid); pass ``gpu=-1`` to force CPU (the engine then turns
|
||||
mixed precision off). The result is read back from
|
||||
``<out>/P000/<group>.h5`` (an ITK-form DisplacementFieldTransform on the
|
||||
T1c grid) and direction-checked by :func:`_orient_impact` (skull-ring
|
||||
gap vs the reverse-proxy field).
|
||||
Preconditions (else :class:`PipelineUnavailable`): the ``docker`` CLI,
|
||||
the ``vboussot/elastix_impact`` image, and the model assets under the
|
||||
cache (``data/impact_models``, auto-created; see the exact download
|
||||
commands in the error).
|
||||
"""
|
||||
import time
|
||||
|
||||
ok, note = _impact_cli_ok()
|
||||
ok, note = _impact_docker_ok()
|
||||
if not ok:
|
||||
raise PipelineUnavailable(f"impact: {note}")
|
||||
root = _impact_elastix_root()
|
||||
if root is None:
|
||||
raise PipelineUnavailable(
|
||||
"impact: no runnable elastix-impact binary on this host (the stock "
|
||||
"build needs glibc >= 2.35 + LibTorch 2.8.0). Run "
|
||||
"scripts/setup_impact_konfai.sh once, or point KONFAI_ELASTIX_DIR "
|
||||
"at a working install.")
|
||||
preset = preset or IMPACT_DEFAULT_PRESET
|
||||
model_rels = _impact_models_host()
|
||||
|
||||
# fixed=T1c (MR), moving=CT: see the docstring (contract direction comes
|
||||
# out of the preset directly; MR_CT convention = the shared rule)
|
||||
# full volumes, no mask / crop / pad; the T1c is the fixed image (shared
|
||||
# rule). No manual init -- elastix's AutomaticTransformInitialization
|
||||
# (CenterOfGravity) aligns the image centers.
|
||||
fixed, moving = fixed_moving(ct, t1c)
|
||||
|
||||
# GPU: auto-spread across the host GPUs by pid, or forced value
|
||||
|
|
@ -930,78 +916,85 @@ Fixed/moving: the pair is registered in the ImpactReg **MR_CT**
|
|||
ngpu = _n_gpus()
|
||||
gpu = (os.getpid() % ngpu) if ngpu else -1
|
||||
|
||||
# per-level feature-extraction scales (6/3/2/2 mm in the moving image's
|
||||
# base-spacing units)
|
||||
base = float(np.median(moving.GetSpacing()))
|
||||
n = len(IMPACT_LEVELS)
|
||||
voxel_sizes = [base * scale for _, scale, *_ in IMPACT_LEVELS]
|
||||
|
||||
# per-case scratch on a local (non-NFS) tmp fs: Data/ + Out/
|
||||
tmp = tempfile.mkdtemp(prefix="impact_reg_")
|
||||
tail = ""
|
||||
try:
|
||||
fixed_path = os.path.join(tmp, "Fixed.mha")
|
||||
moving_path = os.path.join(tmp, "Moving.mha")
|
||||
datadir = os.path.join(tmp, "Data")
|
||||
outdir = os.path.join(tmp, "Out")
|
||||
sitk.WriteImage(sitk.Cast(fixed, sitk.sitkFloat32), fixed_path, True)
|
||||
sitk.WriteImage(sitk.Cast(moving, sitk.sitkFloat32), moving_path, True)
|
||||
os.makedirs(datadir, exist_ok=True)
|
||||
os.makedirs(outdir, exist_ok=True)
|
||||
try:
|
||||
sitk.WriteImage(sitk.Cast(fixed, sitk.sitkFloat32),
|
||||
os.path.join(datadir, "Fixed_image.mha"), True)
|
||||
sitk.WriteImage(sitk.Cast(moving, sitk.sitkFloat32),
|
||||
os.path.join(datadir, "Moving_image.mha"), True)
|
||||
param_text = _impact_param_map(gpu, iters, samples, voxel_sizes)
|
||||
with open(os.path.join(datadir, "ParameterMap.txt"), "w") as f:
|
||||
f.write(param_text)
|
||||
|
||||
cmd = [IMPACT_CLI, "register", preset,
|
||||
"-f", fixed_path, "-m", moving_path,
|
||||
"-o", outdir,
|
||||
"--fields-only", # we derive the warp ourselves
|
||||
"--tmp-dir", os.path.join(tmp, "Work")]
|
||||
# debug aid: keep a persistent copy of the exact parameter map elastix
|
||||
# is about to run, written BEFORE the docker call so an abort leaves it
|
||||
# behind for inspection (per-pid name: parallel workers do not clobber
|
||||
# each other's dumps)
|
||||
dump_dir = d("data/m6reg/impact/paramdump")
|
||||
os.makedirs(dump_dir, exist_ok=True)
|
||||
param_dump = os.path.join(dump_dir, f"ParameterMap_MIND+TS_{os.getpid()}.txt")
|
||||
with open(param_dump, "w") as f:
|
||||
f.write(param_text)
|
||||
|
||||
cmd = ["docker", "run", "--rm"]
|
||||
if gpu >= 0:
|
||||
cmd += ["--gpu", str(gpu)]
|
||||
# preset tuning (YAML values, forwarded by the CLI to the preset app):
|
||||
# the M6 robustness overrides, always; the budget knobs, when set
|
||||
# (no-op for presets whose per-resolution matrix wins)
|
||||
ov = "[" + ",".join(f'"{o}"' for o in IMPACT_PARAM_OVERRIDES) + "]"
|
||||
cmd += ["--set", f"parameter_overrides={ov}"]
|
||||
if iters is not None:
|
||||
cmd += ["--set", f"max_iterations={int(iters)}"]
|
||||
if samples is not None:
|
||||
cmd += ["--set", f"spatial_samples={int(samples)}"]
|
||||
|
||||
env = os.environ.copy()
|
||||
env["KONFAI_ELASTIX_DIR"] = root
|
||||
|
||||
cmd += ["--gpus", "all"]
|
||||
cmd += [
|
||||
"-v", f"{datadir}:/Data",
|
||||
"-v", f"{outdir}:/Out",
|
||||
"-v", f"{d(IMPACT_MODEL_CACHE)}:/Data/Models",
|
||||
IMPACT_IMAGE, IMPACT_ELASTIX_BIN,
|
||||
"-f", "/Data/Fixed_image.mha", "-m", "/Data/Moving_image.mha",
|
||||
"-p", "/Data/ParameterMap.txt",
|
||||
"-out", "/Out/", "-threads", str(threads),
|
||||
]
|
||||
t0 = time.time()
|
||||
proc = subprocess.run(cmd, capture_output=True, text=True,
|
||||
timeout=7200, env=env)
|
||||
proc = subprocess.run(cmd, capture_output=True, text=True, timeout=7200)
|
||||
dt = time.time() - t0
|
||||
tail = "\n".join((proc.stdout + proc.stderr).splitlines()[-25:])
|
||||
if proc.returncode != 0:
|
||||
raise RuntimeError(f"impact: {IMPACT_CLI} failed "
|
||||
f"(rc={proc.returncode}):\n{tail}")
|
||||
tail = "\n".join((proc.stdout + proc.stderr).splitlines()[-25:])
|
||||
raise RuntimeError(f"impact: docker elastix failed (rc={proc.returncode}):\n{tail}")
|
||||
|
||||
case_dirs = sorted(p for p in os.listdir(outdir)
|
||||
if os.path.isdir(os.path.join(outdir, p)))
|
||||
if not case_dirs:
|
||||
raise RuntimeError(f"impact: no case output under {outdir}:\n{tail}")
|
||||
case_dir = os.path.join(outdir, case_dirs[0])
|
||||
entries = [p for p in os.listdir(case_dir)
|
||||
if os.path.isfile(os.path.join(case_dir, p))
|
||||
and not p.startswith("Moved")]
|
||||
if len(entries) != 1:
|
||||
raise RuntimeError(
|
||||
f"impact: expected exactly one transform output in {case_dir}, "
|
||||
f"got {entries}:\n{tail}")
|
||||
T_main, dvf_img = _impact_read_transform(os.path.join(case_dir, entries[0]))
|
||||
T, inverted = _orient_impact(ct, t1c, T_main, dvf_img)
|
||||
itk_tf = os.path.join(outdir, "TransformParameters.0.itk.txt")
|
||||
if not os.path.exists(itk_tf):
|
||||
tail = "\n".join((proc.stdout + proc.stderr).splitlines()[-25:])
|
||||
raise RuntimeError(f"impact: no TransformParameters.0.itk.txt produced:\n{tail}")
|
||||
|
||||
# the optimized Euler (world frame) is the full combination transform
|
||||
# (AutomaticTransformInitialization is folded in by elastix); with
|
||||
# fixed=T1c / moving=CT the direction it holds is resolved empirically
|
||||
# by fix_orientation against the skull ring.
|
||||
T_main = sitk.ReadTransform(itk_tf)
|
||||
T, inverted = fix_orientation(ct, t1c, T_main)
|
||||
finally:
|
||||
if not keep_scratch:
|
||||
shutil.rmtree(tmp, ignore_errors=True)
|
||||
|
||||
models = " + ".join(model_rels)
|
||||
meta = {
|
||||
"pipeline": "impact",
|
||||
"toolkit": (f"impact-reg-konfai (KonfAI; elastix-impact 5.2, "
|
||||
f"preset {preset})"),
|
||||
"metric": ("IMPACT semantic (MIND R1D2_3D L1 + TotalSegmentator M730 "
|
||||
"deepest-layer Dice, per-level weights, Static) + "
|
||||
"Mattes MI (w 0.4) + bending energy"),
|
||||
"transform": (f"Deformable (preset-native B-spline DVF on the T1c grid; "
|
||||
f"CenterOfGravity auto init; "
|
||||
"toolkit": f"Docker {IMPACT_IMAGE} (Elastix 5.2.0 + IMPACT)",
|
||||
"metric": (f"IMPACT semantic ({models}, L1+Dice, Static) + "
|
||||
f"Mattes MI (w {IMPACT_MI_WEIGHT})"),
|
||||
"transform": (f"Rigid (EulerTransform, ASGD, {n} scales, auto center init; "
|
||||
f"device={'GPU' + str(gpu) if gpu >= 0 else 'CPU'})"),
|
||||
"convention": "T1c->CT (verified)", "auto_inverted": inverted,
|
||||
"fixed": _modality_name(fixed), "moving": _modality_name(moving),
|
||||
"seconds_reg": round(dt, 1),
|
||||
"preset": preset, "gpu": gpu, "iters": iters, "samples": samples,
|
||||
"parameter_overrides": list(IMPACT_PARAM_OVERRIDES),
|
||||
"elastix_root": os.path.basename(root),
|
||||
"docker_image": IMPACT_IMAGE, "model": models, "gpu": gpu,
|
||||
"iters": list(iters) if not isinstance(iters, int) else iters,
|
||||
"samples": samples, "param_dump": param_dump,
|
||||
}
|
||||
if keep_scratch:
|
||||
meta["scratch"] = tmp
|
||||
|
|
@ -1272,21 +1265,18 @@ def availability():
|
|||
else:
|
||||
out[name] = (True, f"Docker {SYNTHMORPH_IMAGE}")
|
||||
elif name == "impact":
|
||||
ok, note = _impact_cli_ok()
|
||||
ok, note = _impact_docker_ok()
|
||||
if not ok:
|
||||
out[name] = (False, note)
|
||||
continue
|
||||
root = _impact_elastix_root()
|
||||
if root is None:
|
||||
out[name] = (False,
|
||||
note + "; no runnable elastix-impact binary "
|
||||
"(needs glibc >= 2.35 + LibTorch 2.8.0) -- run "
|
||||
"scripts/setup_impact_konfai.sh once")
|
||||
else:
|
||||
out[name] = (True,
|
||||
note + "; elastix-impact under "
|
||||
f"{os.path.basename(root)}; preset "
|
||||
f"{IMPACT_DEFAULT_PRESET}")
|
||||
try:
|
||||
rels = _impact_models_host()
|
||||
except PipelineUnavailable as e:
|
||||
out[name] = (False, str(e))
|
||||
continue
|
||||
out[name] = (True, f"Docker {IMPACT_IMAGE} + {len(rels)} models "
|
||||
f"in {d(IMPACT_MODEL_CACHE)} (rigid MIND+TS "
|
||||
f"static config)")
|
||||
except Exception as e: # noqa
|
||||
out[name] = (False, repr(e))
|
||||
return out
|
||||
Loading…
Reference in a new issue