- Updated `05_monai_run_iterative.py` to include legacy mode and improved configuration options for training.
- Introduced `UNetBAligned` class in `monai_common.py` for deep supervision and instance normalization.
- Modified `train_transform` and `make_dataloader` in `monai_common.py` to support B-aligned data protocol with Z-score normalization and augmentation.
- Added new loss functions `nn_dc_and_ce` and `b_dc_and_ce` in `losses.py` for improved training performance.
- Enhanced `Unet3D` architecture in `unet3d.py` to support leaky ReLU and deep supervision.
- Created new augmentation functions in `augment_b.py` for Pipeline B's training, including rotation, scaling, and noise.
- Implemented new training scripts `run_b_100ep_stages.sh` and `run_c_bloss_100ep_after_b.sh` for structured training and evaluation.
- Updated dataset handling in `dataset.py` to accommodate new augmentation and normalization strategies.
- Implemented `qa_label_interpolation.py` to evaluate the registration of T1c and CT images using various methods (rigid, affine, and SyN).
- The script generates warped TV images, visualizations, and summary metrics for each registration method.
- Outputs include PNG figures for visual comparison, a summary JSON file with metrics, and cached registration transforms.
- Added functionality for volume preservation checks and detailed logging of processing steps.
Extend registration_pipelines and bench_registration with stock antspyx
ANTs presets (SyNRA, SyN, Repro sr/br, QuickRepro sr/br) plus the legacy
Rigid -> SyNOnly workflow, and update the deformable benchmark
documentation with follow-up ANTs-variant runs and results.
