Introduce src/exclusions.py (rules: total TV > 1e6 mm3, single target
< 100 mm3, non-brain NTUH keys) and scripts/make_tv_slices.py, a
first-stage curation script that classifies every labeled row right
after the source fetch and writes TV-slice QA figures to
data/tv_slices/{inclusion,exclusion}/.
05_build_splits.py re-applies the same rules after split assignment:
kept rows retain their split, excluded subjects still count in the
subject-level shuffle boundaries, and the audit trail is written to
data/manifests/excluded.jsonl. Label stats run in a
ProcessPoolExecutor (32 workers).
Move proc_row into src/common.py and add labeled_source_rows() so the
curation stage and the split builder share one processed-row view.
Document the 143-row exclusion audit and the interim 10-epoch
benchmark results in BENCHMARKING.md; update the README data layout
and script tables.
- 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.
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.
