Xiao Furen
0274e954be
Refactor the cylinder parameter selection and optimization process to improve accuracy and robustness. - Implement `snap_to_discrete_values_xfr` using a KDTree for efficient mapping of continuous diameter and length values to a predefined set of discrete points. - Update `objective_function_xfr` to use the new snapping mechanism and introduce a weighted loss component for diameter and length. - Adjust PSO optimization bounds and search ranges in `run_pso_torch_xfr` to better align with image dimensions and anatomical constraints. - Refine scoring logic in `cl_score_torch_xfr` with updated penalty weights for overlaps and out-of-bone voxels. - Update `config/constant.py` with new allowed diameter and length ranges. - Improve `imaging/preprocessing.py` by making `PROGRESS_FILE` a parameter to allow per-output-directory progress tracking. - Update `xfr_debug.py` with improved error handling and directory paths for batch processing.
24 lines
No EOL
652 B
Python
24 lines
No EOL
652 B
Python
import os
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from imaging.preprocessing import process_dataset
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data_root = '/mnt/1220/Public/dataset/Spine/CTSpine1K/data/'
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label_root = '/mnt/1220/Public/dataset/Spine/CTSpine1K/label/'
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output_dir = '/mnt/1248/open2/cyrou/CBT/Seg/Resample/standardized-xfr-2/'
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label_map = {
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'colon': 'conlon',
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'COVID-19': 'COVID-19',
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'HNSCC-3DCT-RT_neck': 'HNSCC-3DCT-RT_neck',
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'liver': 'Liver',
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}
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def main():
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for key, value in label_map.items():
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data_dir = os.path.join(data_root, key)
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label_dir = os.path.join(label_root, value)
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process_dataset(data_dir, label_dir, output_dir)
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if __name__ == '__main__':
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main() |