Furen Xiao
695bbb6e11
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.
31 lines
No EOL
1.2 KiB
Python
31 lines
No EOL
1.2 KiB
Python
"""Build/rebuild the nnU-Net raw dataset (Dataset210_NTUH_T1C_PL) from rows jsonl.
|
|
|
|
rows: {key, img, label} where img is the NATIVE (unprocessed) T1c nifti and
|
|
label a nifti mask on (or warpable to) that grid. No resampling/cropping/
|
|
normalization is applied here — nnUNetv2_plan_and_preprocess (02) does the
|
|
preprocessing; imagesTr entries are symlinks to the native volumes, labelsTr
|
|
entries are symlinks when already 0/1 on the image grid, else nearest-warped
|
|
+ binarized copies (label fix-up only).
|
|
|
|
Usage: python scripts_nnu/01_nnu_prepare_dataset.py --rows <rows.jsonl>
|
|
"""
|
|
import argparse
|
|
import os
|
|
import sys
|
|
sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
|
|
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
|
|
from src.common import load_jsonl
|
|
from nnu_common import make_raw_dataset, raw_ds
|
|
|
|
|
|
def main():
|
|
ap = argparse.ArgumentParser()
|
|
ap.add_argument("--rows", required=True, help="jsonl with {key, img, label} (native paths)")
|
|
args = ap.parse_args()
|
|
rows = load_jsonl(args.rows)
|
|
n = make_raw_dataset(rows)
|
|
print(f"[nnu:prepare] {raw_ds()}: {n} cases", flush=True)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main() |