对比了三种皮肤分割方法，帮医生选对医学影像分析工具
3D Skin Segmentation Methods in Medical Imaging: A Comparison
- 用迭代区域生长和深度学习模型比较分割效果
- AI在CT图像中自动去床架，但对MRI表现差
- 图形法适合MRI但噪声多，适合追求精度的场景
自动分割解剖结构在医学影像分析中至关重要，有助于诊断与治疗规划。皮肤分割在多模态影像配准与可视化中起关键作用。3D皮肤分割支持个性化医疗、手术规划与远程监测，可生成真实患者模型用于治疗模拟、操作可视化和病情持续追踪。本文分析并比较了算法与AI驱动的皮肤分割方法，强调根据数据可用性和应用需求选择策略的关键因素。我们评估了迭代区域生长算法与TotalSegmentator（基于深度学习的方法）在不同成像模态和解剖区域的表现。测试显示，AI分割在自动化方面表现优异，但在以CT训练的模型上对MRI处理效果不佳；而图形方法在MRI上表现更优，但引入更多噪声。此外，AI能自动移除CT中的患者床架，图形法则需手动干预。
原文摘要 · Abstract (English)
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.
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