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SegTom:用于胸腔腹部多器官解剖结构的3D体积医学图像分割框架

Yan Pang, Yunhao Li, Jiaming Liang

IEEE journal of biomedical and health informatics

|September 5, 2025

概括

在医疗图像中准确分隔胸腔腹部结构. 这种先进的工具通过高效地捕捉复杂的解剖学来改善诊断和治疗计划.

科学领域:

医学成像分析
计算解剖学
医疗保健中的人工智能

背景情况:

对于临床决定而言,胸腹部结构的准确细分至关重要.
现有的方法在处理复杂和多样化的解剖数据方面面临挑战.
低于最佳的细分影响诊断,治疗计划和疾病管理.

研究的目的:

介绍 SegTom,一个新的体积细分框架.
解决目前胸腹部细分技术的局限性.
增强医学成像中复杂解剖学表示的捕获.

主要方法:

开发了SegTom,一个具有专用SegTom块的框架.
在 SegTom 块内包含层次的解剖表示分解.
通过分解和聚合自我注意机制优化信息交换.

主要成果:

在九个不同的CT和MRI数据集中验证了SegTom.
在细分各种解剖结构方面取得了高性能.
显示心脏MRI细分的平均子相似系数 (DSC) 为87. 29%.
在腹部CT多器官细分中报告了83. 48%的DSC.
在专用气道CT细分中实现92.01%的DSC.

结论:

SegTom提供了一个强大而高效的解决方案,用于胸腔腹部细分.
该框架在多种成像模式和解剖学目标中表现出卓越的性能.
SegTom显示了改善临床决策和患者结果的巨大潜力.

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