Update M6 manifest building to register T1c to CT with rigid
Mattes MI on a CT-space head box, including multi-start initial
tilts for large inter-session head motion. The inverse transform
warps CT and GTV into T1c native space, with acceptance based on
concentricity of the warped CT skull ring and MR head silhouette.
Rerun preprocessing when a row has a label but the warped label file
is missing. Normalize volume orientation in read_nii_img.
Add registration benchmark documentation, benchmark script, and
shared registration pipeline helpers.
Add tof, sub, and subtraction tokens to the Lee T1c exclusion regex.
This prevents time-of-flight MRA and post/pre subtraction sequences
from matching T1-based names.
Fiesta/CISS/SSFP are balanced-SSFP (T2-dominant) sequences whose
short TE fools the seq/TE fallback, causing T1c misclassification.
Add them to the exclusion regex and guard against empty candidates.
Expand write_excluded_notes to enumerate all series in a timepoint
(not just T1c candidates) and parse DICOM txt metadata for each
series' description and protocol, producing richer no-T1c reports.
- Updated regex patterns for improved matching of series names and tags.
- Added functionality to reject non-head series based on study and series descriptions.
- Implemented max voxel spacing check to filter out series with excessive spacing.
- Enhanced the reconstruction script to handle dynamic-frame series exclusions and artifact pruning.
- Modified output paths for reconstructed NIfTI files and added QA screenshot generation.
- Improved argument parsing in benchmark and pseudo-labeling scripts for better flexibility.
- Introduced a new script for generating QA screenshots from reconstructed volumes.
Add a new benchmarking script and documentation to facilitate
performance comparisons between the different implemented pipelines.
- Create `scripts/benchmark_pipelines.py` for automated evaluation
- Add `BENCHMARKING.md` to outline benchmarking procedures and metrics
Update the nnU-Net pipeline to operate on native (unprocessed) volumes
instead of preprocessed ones. This allows nnU-Net to utilize its own
`plan_and_preprocess` logic for resampling, cropping, and normalization,
ensuring Pipeline B remains distinct from Pipelines A and C.
- Update `scripts/05_build_splits.py` to include native `img` and `label`
paths in the manifest rows.
- Modify `scripts_nnu/01_nnu_prepare_dataset.py` to symlink native
volumes and implement a label fix-up mechanism for non-conforming grids.
- Update `scripts_nnu/04_nnu_pseudo_label.py` and `05_nnu_eval_test.py`
to use native image paths and perform selection/evaluation in physical
mm on the native grid.
- Refactor `scripts_nnu/nnu_common.py` to handle native-grid label
alignment and volume-based selection gates.
- Update `README.md` to document the preprocessing differences between
Pipelines A/C and Pipeline B.
Introduce the third segmentation pipeline using MONAI (1.6.x) to allow
direct comparison with Pipelines A and B. This includes the implementation
of the iterative pseudo-labeling workflow, training scripts, and
inference protocols.
- Add `scripts_monai/` directory containing the MONAI pipeline scripts.
- Update documentation in `README.md` and `AGENTS.md` to include MONAI
package requirements and pipeline details.
- Configure `.gitignore` to exclude MONAI-specific run directories.
- Update data directory descriptions to include MONAI pseudo-labels.
Update README.md to include detailed project overview, environment
requirements, data sources, and a comprehensive directory layout.
Add nnU-Net pipeline documentation and directory descriptions.
Update .gitignore to exclude nnU-Net specific directories and add
new scripts directory for nnU-Net pipeline.
Add initial nnU-Net pipeline scripts.
Enhance the training pipeline with stateful checkpointing and improve
the resilience of the data loading process against filesystem latency
and transient I/O errors.
- Implement auto-resuming in `train_ddp` by loading model, optimizer,
and scheduler states from `state.pt`.
- Add atomic state saving using temporary files to prevent corruption.
- Introduce `_read_nii` with exponential backoff retries to handle
transient NFS/filesystem failures during NIfTI reading.
- Add explicit error handling for missing or unreadable label files in
`PatchDataset`.
- Update `sliding_window_probs` to conditionally apply Test-Time
Augmentation (TTA) based on the `tta` parameter.
- Add `scripts/test_dataloader.py` for verifying dataset integrity.
Refactor the data loading and preprocessing pipeline to handle edge cases in
medical imaging data, including NaN/Inf values, shape mismatches, and
numerical instability during training.
- Update `01_build_ntuh_manifest.py` with improved regex for T1c detection,
spine exclusion, and deduplication logic based on acquisition timestamps.
- Enhance `PatchDataset` in `src/dataset.py` to handle NaN/Inf values,
clip intensity ranges, and ensure label/image shape alignment via
padding/trimming.
- Add a zero-gradient fallback in `src/training.py` to prevent DDP
synchronization failures when encountering NaN/Inf losses.
- Add `scripts/scan_procs.py` for process monitoring.
- Increase DataLoader timeout to prevent hangs during heavy I/O.
