使用堆叠的U-Net进行大动脉和主要肺动脉细分,并在非对比增强的计算机断层扫描图像上进行定位

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Hidenobu Suzuki¹, Yoshiki Kawata², Keiju Aokage³

¹Faculty of Science and Technology, Tokushima University, Tokushima, Japan.

Medical physics

|July 31, 2023

概括

一种新的堆叠U-Net模型在非对比增强CT (NCE-CT) 扫描中改善了主动脉和主要肺动脉 (MPA) 的细分,即使在附近的结构中也是如此. 这种技术在诊断慢性血栓栓塞性肺高血压 (CTEPH) 中表现有前途.

科学领域:

医疗成像医学成像
人工智能在医学中的应用
心血管成像 - 心血管成像

背景情况:

在非对比增强型计算机断层扫描 (NCE-CT) 图像中对大动脉和主要肺动脉 (MPA) 的细分具有挑战性,原因是与食道等邻近结构接触.
准确的细分对于诊断诸如慢性血栓栓塞性肺高血压 (CTEPH) 等疾病至关重要.

研究的目的:

开发和评估一个两阶段堆叠的U-Net模型,结合本地化技术,以改善NCE-CT图像中大动脉和MPA的细分.
评估模型在血管和食道接触的情况下的性能,以及其在分类CTEPH病例中的实用性.

主要方法:

采用了两阶段堆叠的U-Net架构,集成2D U-Nets和3D U-Net,具有多个输入通道.
使用局部化技术来提取相关的解剖信息进行细分.
该模型经过CTEPH和没有CTEPH的受试者的NCE-CT图像的训练和验证,使用10倍交叉验证.
用子相似系数 (DSC) 和平均表面距离 (MSD) 评估细分精度,并进行统计分析,包括多次比较校正.

主要成果:

堆叠的U-Net模型实现了对细分的大动脉和MPA的平均表面距离 (MSD) 约为1像素,即使有显著的接触面 (CSR>=0.4).
与其他U-Net架构相比,在非CTEPH和CTEPH情况下,非接触面的MSD值显著较低 (p <0.05).
该模型在基于细分血管直径的基础上对CTEPH和非CTEPH病例进行分类时显示出高准确性,右主肺动脉直径的ROC曲线下的面积为0.97.

结论:

血管和食道接触显著影响NCE-CT图像上的大动脉和MPA细分.
提出的堆叠U-Net和本地化方法有效地减轻了这些联系人的影响,提高了细分的准确性.
这种方法有可能通过NCE-CT成像改善CTEPH的诊断.

关键词:

大动脉和主要的肺动脉.非对比增强型计算机断层扫描无对比增强计算机断层扫描细分化细分化的细分化

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