End-to-End Medical Imaging Pipeline

This workflow utilizes the MONAI framework to process 3D medical CT volumes for binary organ segmentation. The pipeline transforms raw medical data into a format suitable for deep learning by applying specific medical imaging operations: orientation alignment, voxel-spacing normalization, intensity windowing, and foreground cropping.

Model Training and Inference Techniques

To handle the computational demands of 3D volumetric data, the implementation employs several key strategies:

• Patch-based Sampling: Instead of processing full volumes, the model trains on smaller, randomly cropped patches (96x96x96) to manage memory constraints.
• Mixed Precision Training: Uses PyTorch's autocast and GradScaler to accelerate training and reduce GPU memory usage.
• Sliding-Window Inference: During validation and testing, the model performs inference using a sliding-window approach with 50% overlap to ensure seamless segmentation across the entire 3D volume.
• Loss and Optimization: The model uses DiceCELoss to handle class imbalance and an AdamW optimizer with a CosineAnnealingLR scheduler for stable convergence.

Evaluation and Visualization

Model performance is tracked using the Dice metric, which measures the overlap between predicted masks and ground-truth labels. The tutorial includes a final visualization step that compares original CT slices, ground-truth labels, and model predictions, allowing for qualitative assessment of the segmentation accuracy.