Refactor the preprocessing and segmentation pipeline to handle AP orientation
variations and improve anatomical boundary detection.
Key changes include:
- Implement automated AP orientation detection in `process_single_image`
to handle prone scans by flipping CT and labels when necessary.
- Enhance `segment_spinous_process` using a gap-based approach to identify
the spinal canal, providing more stable thresholds for spinous process
and vertebral body segmentation.
- Improve optimization search space by using the vertebral body (VBODY)
projection for x/z bounding box calculation instead of the whole bone.
- Refactor `render_bone_figure` to unify 2D/3D visualization and support
detailed anatomical coloring (VBODY, spinous process).
- Update `cl_score_torch_xfr` with more robust penalty handling for
out-of-bone and null-voxel regions.
- Add `retry_robust` utility to handle transient NFS file system errors.
- Update `xfr_preprocess.py` to include anatomical segmentation coloring
in rotated level visualizations.
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.
