3D Skin Segmentation Methods in Medical Imaging: A Comparison

TL;DR

This paper compares algorithmic and AI-driven 3D skin segmentation methods in medical imaging, highlighting their strengths and limitations across modalities like MRI and CT for applications in personalized medicine and surgical planning.

Contribution

It provides a comprehensive comparison of iterative region-growing and deep learning-based segmentation approaches across multiple imaging modalities and anatomical regions.

Findings

AI segmentation performs well in automation but has limitations with MRI data.

Region-growing methods handle MRI better but are noisier.

AI methods automate patient bed removal in CT images.

Abstract

Automatic segmentation of anatomical structures is critical in medical image analysis, aiding diagnostics and treatment planning. Skin segmentation plays a key role in registering and visualising multimodal imaging data. 3D skin segmentation enables applications in personalised medicine, surgical planning, and remote monitoring, offering realistic patient models for treatment simulation, procedural visualisation, and continuous condition tracking. This paper analyses and compares algorithmic and AI-driven skin segmentation approaches, emphasising key factors to consider when selecting a strategy based on data availability and application requirements. We evaluate an iterative region-growing algorithm and the TotalSegmentator, a deep learning-based approach, across different imaging modalities and anatomical regions. Our tests show that AI segmentation excels in automation but struggles with MRI due to its CT-based training, while the graphics-based method performs better for MRIs but introduces more noise. AI-driven segmentation also automates patient bed removal in CT, whereas the graphics-based method requires manual intervention.