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胸部CT数据集上心血管结构的自动细分:总细分器的更新

Daniel Hinck¹, Martin Segeroth¹, Jules Miazza²

¹Department of Radiology, University Hospital Basel, University of Basel, Petersgraben 4, 4031 Basel, Switzerland.

European journal of radiology

|February 21, 2025

概括

现在,TotalSegmentator工具具有增强的心血管 (CV) 结构细分功能,提高了对大动脉动脉瘤等疾病的研究的准确性. 这些进步得到了验证,并在标准计算机上运行.

科学领域:

放射学和医学成像学医学成像学
计算解剖学的计算解剖学
心血管研究研究心血管研究

背景情况:

在放射学例行中,定量分析至关重要.
总细分器工具为超过104个结构提供细分.
需要增强心血管 (CV) 结构,以更深入地了解疾病.

研究的目的:

在TotalSegmentator中增加缺失的和改进现有的心血管 (CV) 结构.
潜在地帮助未来研究诸如大动脉动脉瘤等疾病.
使用内部和外部数据集验证增强的细分.

主要方法:

利用了来自临床常规的1613个CT扫描的数据集.
在专用成像软件中执行代细分,添加11个结构并增强6.
计算的子相似度得分 (DICE) 和规范表面距离 (NSD) 用于验证.

主要成果:

在内部验证中,获得了0.967的中位数DICE和1000的NSD.
对5/6增强结构的DICE和NSD显著改善 (p < 0.001).
对东阳数据集的外部验证结果显示DICE为0.970和NSD为1.000.

结论:

成功实现了CV结构的准确和增强的细分.
增强的TotalSegmentator适用于研究研究.
该工具在传统计算机上有效运行,有或没有专用GPU.

关键词:

心血管系统心血管系统深度学习 (Deep Learning) 是一种深度学习.胸部胸部胸部胸部整体细分器整体细分器

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