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用深度学习量化腹腔大动脉和主要侧枝的化

J Halkoaho¹, O Niiranen², E Salli³

¹Department of Radiology, HUS Diagnostic Center, Helsinki University Hospital and University of Helsinki, Helsinki, Finland; Department of Physics, University of Helsinki, P.O. Box 64, FI-00014 Helsinki, Finland.

Clinical radiology

|February 16, 2024

概括

一种新的神经网络方法使用计算机断层扫描血管学准确量化腹腔大动脉和分支化. 这种自动化方法与手动细分有很高的相关性,改善了硬化症评估.

科学领域:

医疗成像医学成像
人工智能的人工智能
心血管成像 - 心血管成像

背景情况:

腹腔大动脉化是心血管疾病的标志物.
准确量化化对于风险评估至关重要.
目前用于化量化的方法存在局限性.

研究的目的:

开发和评估一种基于神经网络的方法,用于自动定量腹腔大动脉及其分支化.
为了评估模型的性能与手动地面真实细分相比.

主要方法:

利用了58个计算机断层扫描 (CT) 血管图卷,手动对大动脉,分支和化进行了细分.
训练了两个V-Net组合模型用于动脉和化细分.
算法缩短了动脉分支进行分析.

主要成果:

化细分模型实现了0.69的Dice分数和0.80.8的体积相似性 (VS).
动脉细分表现包括大动脉的Dice分数为0.96,分支为0.74,关动脉为0.72.
观察到自动化和手动量化之间存在很高的相关性.

结论:

介绍的神经网络是第一个自动细分大动脉及其分支以及CT血管学化.
这种自动化技术为依赖于普通CT的传统方法提供了一个有希望的替代方案.
该模型显示了改善腹腔大动脉化和相关疾病的评估的潜力.

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