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腹部多器官细分3D负对比CT胆血管细胞造影:深度学习方法的比较研究

Bin Li¹, Jian Zhang², Hao Fu²

¹Department of Radiology, Jiangnan University Medical Center, Wuxi No.2 People's Hospital, Wuxi, China.

Abdominal radiology (New York)

|November 18, 2025

概括

像Swin-UNETR 3D这样的3D体积模型在3D-nCTCP扫描中显著提高了腹部器官细分的准确性. 这种自动化增强了手术前的规划和诊断,提供比2D模型更稳定,更精确的结果.

科学领域:

医疗成像医学成像
人工智能在医学中的应用
放射学放射学是一门学科.

背景情况:

准确的腹部器官细分对于手术前的规划和诊断至关重要.
目前使用3D-nCTCP对胆汁和胰腺系统进行细分的方法可能耗时且容易变化.

研究的目的:

通过使用3D负对比CT胆血管瘤造影 (3D-nCTCP) 来自动化胆道和胰腺系统的细分.
为了比较最先进的2D和3D深度学习模型对腹部多器官细分的性能.

主要方法:

追溯收集来自111名患者的双相增强CT数据.
专家放射科医生对门阶段数据进行注释.
四个细分模型的实施和比较:TransUNet 2D,nnU-Net 2D,Swin-UNETR 2D和Swin-UNETR 3D.
使用子相似系数 (DSC) 和平均对称表面距离 (ASSD) 的性能评估.

主要成果:

斯温-UNETR 3D显示出卓越的细分性能,特别适用于诸如十二指肠,胰腺和胆道系统等具有挑战性的器官.
斯温-UNETR 3D的3D体积方法优于2D模型,特别是在改善十二指肠的边界定位方面.
在肝脏 (96.12%),胰腺 (81.00%) 和胆道系统 (88.64%) 中获得了高的DSC,在十二指肠 (75.31%) 中取得了竞争性结果.

结论:

3D体积模型,特别是Swin-UNETR 3D,在3D-nCTCP上的准确和稳定的腹部多器官细分方面优于2D模型.
自动化细分减少了手动注释时间,可能有助于更广泛的临床采用.
这些发现支持使用先进的人工智能模型来改善手术前规划和诊断准确度.

关键词:

在腹部,腹部.深度学习是一种深度学习.多机关细分化多机关细分化负对比CT胆血管细胞造影,阴比CT胆血管细胞造影胰腺胆系统这是一个三维的三维空间.

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