This tutorial presents a complete, end-to-end implementation of a 3D medical image segmentation pipeline using MONAI (Medical Open Network for Artificial Intelligence), applied to the Spleen segmentation task from the Medical Segmentation Decathlon Task09 dataset.
Pipeline Overview
The workflow handles volumetric CT scans and incorporates essential medical imaging transformations, including orientation alignment, voxel-spacing normalization, intensity windowing, foreground cropping, and patch-based sampling. A 3D UNet model is then trained for binary organ segmentation.
Key Features
The implementation leverages mixed precision training, DiceCE loss, sliding-window inference, Dice-based validation, and qualitative visualization to analyze model performance and compare predictions with ground-truth masks.
Outcome
By following this guide, you will move from raw medical volumes to a complete train–validate–visualize segmentation system.
via MarkTechPost