-
Notifications
You must be signed in to change notification settings - Fork 0
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
Merge remote-tracking branch 'origin/main' into um/baseline_ivadomed_…
…training
- Loading branch information
Showing
3 changed files
with
178 additions
and
11 deletions.
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,84 @@ | ||
| """ | ||
| Quality control for preprocessing step. | ||
| See `preprocess_data.sh` for the preprocessing pipeline. | ||
| """ | ||
|
|
||
| import argparse | ||
| import os | ||
| from tqdm import tqdm | ||
| from collections import Counter | ||
|
|
||
| import pandas as pd | ||
| import nibabel as nib | ||
| import numpy as np | ||
|
|
||
| # Argument parsing | ||
| parser = argparse.ArgumentParser(description='Quality control for preprocessing.') | ||
| parser.add_argument('-s', '--sct_output_path', type=str, required=True, | ||
| help='Path to the folder generated by `sct_run_batch`. This folder should contain `data_processed` folder.') | ||
| args = parser.parse_args() | ||
|
|
||
| # Quick checking of arguments | ||
| if not os.path.exists(args.sct_output_path): | ||
| raise NotADirectoryError('%s could NOT be found!' % args.sct_output_path) | ||
| else: | ||
| if not os.path.exists(os.path.join(args.sct_output_path, 'data_processed')): | ||
| raise NotADirectoryError('`data_processed` could NOT be found within %s' % args.sct_output_path) | ||
|
|
||
| # Get all subjects | ||
| subjects_df = pd.read_csv(os.path.join(args.sct_output_path, 'data_processed', 'participants.tsv'), sep='\t') | ||
| subjects = subjects_df['participant_id'].values.tolist() | ||
|
|
||
| # Log resolutions and sizes for data exploration | ||
| resolutions, sizes = [], [] | ||
|
|
||
| # Log problematic subjects for QC | ||
| failed_crop_subjects, shape_mismatch_subjects, left_out_lesion_subjects = [], [], [] | ||
|
|
||
| # Perform QC on each subject | ||
| for subject in tqdm(subjects, desc='Iterating over Subjects'): | ||
| # Get paths | ||
| subject_images_path = os.path.join(args.sct_output_path, 'data_processed', subject, 'anat') | ||
| subject_labels_path = os.path.join(args.sct_output_path, 'data_processed', 'derivatives', 'labels', subject, 'anat') | ||
|
|
||
| # Read cropped subject image (i.e. 3D volume) to be used for training | ||
| img_crop_fpath = os.path.join(subject_images_path, '%s_UNIT1_crop.nii.gz' % subject) | ||
| if not os.path.exists(img_crop_fpath): | ||
| failed_crop_subjects.append(subject) | ||
| continue | ||
| img_crop = nib.load(img_crop_fpath) | ||
|
|
||
| # Get and log size and resolution for each subject image | ||
| size = img_crop.get_fdata().shape | ||
| resolution = tuple(img_crop.header['pixdim'].tolist()[1:4]) | ||
| resolution = tuple([np.round(r, 1) for r in list(resolution)]) | ||
| sizes.append(size) | ||
| resolutions.append(resolution) | ||
|
|
||
| # Read original and cropped subject ground-truths (GT) | ||
| gt1_fpath = os.path.join(subject_labels_path, '%s_UNIT1_lesion-manual.nii.gz' % subject) | ||
| gt1_crop_fpath = os.path.join(subject_labels_path, '%s_UNIT1_lesion-manual_crop.nii.gz' % subject) | ||
| gt2_fpath = os.path.join(subject_labels_path, '%s_UNIT1_lesion-manual2.nii.gz' % subject) | ||
| gt2_crop_fpath = os.path.join(subject_labels_path, '%s_UNIT1_lesion-manual2_crop.nii.gz' % subject) | ||
|
|
||
| gt1 = nib.load(gt1_fpath) | ||
| gt1_crop = nib.load(gt1_crop_fpath) | ||
| gt2 = nib.load(gt2_fpath) | ||
| gt2_crop = nib.load(gt2_crop_fpath) | ||
|
|
||
| # Basic shape checks | ||
| if not img_crop.shape == gt1_crop.shape == gt2_crop.shape: | ||
| shape_mismatch_subjects.append(subject) | ||
| continue | ||
|
|
||
| # Check if the dilated SC mask leaves out any lesions from GTs (from each rater) | ||
| if not (np.allclose(np.sum(gt1.get_fdata()), np.sum(gt1_crop.get_fdata())) and | ||
| np.allclose(np.sum(gt2.get_fdata()), np.sum(gt2_crop.get_fdata()))): | ||
| left_out_lesion_subjects.append(subject) | ||
|
|
||
| print('RESOLUTIONS: ', Counter(resolutions)) | ||
| print('SIZES: ', Counter(sizes)) | ||
|
|
||
| print('Could not find cropped image for the following subjects: ', failed_crop_subjects) | ||
| print('Found shape mismatch in images and GTs for the following subjects: ', shape_mismatch_subjects) | ||
| print('ALERT: Lesion(s) from raters cropped during preprocessing for the following subjects: ', left_out_lesion_subjects) |