A Coding Implementation on MONAI for End-to-End 3D Spleen Segmentation Using UNet on Medical CT Volumes

This tutorial demonstrates building a comprehensive 3D medical image segmentation pipeline using MONAI. The focus is on segmenting the spleen from volumetric CT scans using the Medical Segmentation Decathlon Task09 dataset. This involves a complete workflow from raw medical volumes to a fully functional segmentation system encompassing training, validation, and visualization.

The process integrates various medical imaging transformations. These include orientation alignment, voxel-spacing normalization, intensity windowing, foreground cropping, and patch-based sampling, all crucial steps for preparing the data for model training.

A 3D UNet model is then trained for binary organ segmentation. The training regimen incorporates mixed-precision training, DiceCE loss, and sliding-window inference. Evaluation is performed using Dice-based validation and qualitative visualization, providing insights into model learning and prediction accuracy against ground-truth masks. The tutorial also covers the installation of necessary MONAI dependencies and the configuration of key parameters such as device settings, dataset directories, and patch sizes. It further details the creation of preprocessing pipelines and the setup of training and validation data loaders.