aiqbench takeMachine Learning

This tutorial provides a practical implementation of an end-to-end 3D segmentation pipeline using MONAI to isolate spleen structures from volumetric CT scans. By leveraging standard architectures like the 3D UNet alongside medical-specific transforms and mixed-precision training, it demonstrates how developers can build robust systems for binary organ segmentation tasks.

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

MarkTechPostJune 12, 20263 min read

In this tutorial, we build an end-to-end 3D medical image segmentation pipeline using MONAI to segment the spleen on the Medical Segmentation Decathlon Task09 dataset. We work with volumetric CT scans, apply medical imaging transformations such as orientation alignment, voxel-spacing normalization, intensity windowing, foreground cropping, and patch-based sampling, and then train a 3D UNet model […] The post A Coding Implementation on MONAI for End-to-End 3D Spleen Segmentation Using UNet on Medical CT Volumes appeared first on MarkTechPost.

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