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Pull request overview
This PR updates BraTS METs example outputs and notebook guidance to reflect BraTS 2026 segmentation metric parsing behavior, including updated FP/FN penalization examples and an added SEG parsing example.
Changes:
- Updates the METs summary CSV with revised small-lesion FN/F1 values.
- Adds a SEG summary CSV generated from the sample metrics JSON.
- Expands the METs notebook with BraTS 2026 thresholds and a SEG parsing section.
Reviewed changes
Copilot reviewed 3 out of 3 changed files in this pull request and generated 6 comments.
| File | Description |
|---|---|
example/sample_seg_summary.csv |
Adds sample output for the seg parser. |
example/sample_mets_summary.csv |
Updates METs parser sample metrics for the revised FN handling. |
example/brats_mets.ipynb |
Updates notebook commands, saved outputs, thresholds, and parsing documentation. |
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| "- **Lesion-wise Segmentation Metrics for Large Lesions**\n", | ||
| "- **Detection Metrics for Small Lesions**\n", | ||
| "\n", | ||
| "As shown above, the last three rows of the DataFrame conveniently provide the **mean** and **standard deviation (std)** and **median** for each metric column, calculated over all valid (non-NaN) rows. This gives a quick statistical overview of the algorithm's performance across the entire dataset.\"" |
| "cell_type": "code", | ||
| "source": "!brats-evaluate \\\n --config mets \\\n --ref_path ./sample_data/ref/ \\\n --pred_path ./sample_data/pred/ \\\n --summary_json ./sample_mets_metrics.json", | ||
| "source": [ | ||
| "!python ../brats_evaluation/evaluation.py \\\n", |
| "cell_type": "code", | ||
| "source": "!brats-parse-metrics mets \\\n --json_path ./sample_mets_metrics.json \\\n --output_csv_path ./sample_mets_summary.csv", | ||
| "source": [ | ||
| "!python ../brats_evaluation/metrics_parser.py mets \\\n", |
| "cell_type": "code", | ||
| "source": [ | ||
| "# For simplicity we use the metrics cmoputed from the METs samples:\n", | ||
| "!python ../brats_evaluation/metrics_parser.py seg \\\n", |
| }, | ||
| "cell_type": "code", | ||
| "source": [ | ||
| "# For simplicity we use the metrics cmoputed from the METs samples:\n", |
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| "/home/mehdi/anaconda3/envs/bratseval/lib/python3.10/site-packages/panoptica/metrics/normalized_surface_dice.py:41: UserWarning: The threshold is set to 0.5, which is the default value, which may not be appropriate for your data.\r\n", | ||
| " warnings.warn(\r\n", | ||
| "[2/4] Evaluating BraTS-MET-00217-000-seg.nii.gz...\r\n", | ||
| "[3/4] Evaluating BraTS-MET-00630-001-seg.nii.gz...\r\n", | ||
| "/home/mehdi/anaconda3/envs/bratseval/lib/python3.10/site-packages/panoptica/utils/input_check_and_conversion/sanity_checker.py:182: UserWarning: The input arrays are not of integer type. This may lead to unexpected behavior in the segmentation maps.\r\n", |
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The following were updated to meet the requirements for BraTS2026 segmentations:
METsand otherSEGtasks.