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临床导向的深度学习框架用于黑血MRI中自动化血管壁细分:一个多中心研究

Xuetong Tao^1,2, Shuai Shen^3,4, Long Yang^1,2

¹Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.

European radiology

|November 22, 2025

概括

这项研究提出了一个深度学习框架,用于在黑人血液MRI成像中准确地细分血管壁. 该方法改善了用于预防和监测中风的脑血管风险评估.

科学领域:

医疗成像医学成像
人工智能的人工智能
心血管研究研究心血管研究

背景情况:

精确的内和内血管壁的细分对于评估脑血管疾病至关重要.
目前的手动细分方法耗时,容易引起观察者之间的变化.
黑血磁共振血管壁成像 (MR-VWI) 提供了详细的血管壁信息,但需要强大的细分技术.

研究的目的:

开发和验证一种临床适用的深度学习框架,用于对内和状血管壁进行自动细分.
为了提高黑人血型MR-VWI血管壁细分的准确性和可重复性.
为简化脑血管风险评估提供一个实用的工具.

主要方法:

一项涉及193名患者和高分辨率黑血MR-VWI数据的回顾性多中心研究.
开发一个深度学习框架,包括极坐标映射,功能共享填充和极子损失函数.
在独立的多中心数据集和MICCAI 2021船壁细分挑战数据集上的外部验证;使用Grad-CAM的可解释性.

主要成果:

深度学习模型在外部测试集上实现了高细分精度,子相似系数 (DSC) 为0.928 (外壁),0.936 (光线) 和0.844 (容器壁).
该模型在边界和区域准确性方面明显优于四个基准网络.
它在公开的MICCAI数据集中实现了最高的血管壁DSC (0.782),并通过Grad-CAM显示了对相关解剖边界的持续关注.

结论:

开发的基于深度学习的方法可以在临床黑人血液MR-VWI中准确和可重复的血管壁细分.
这一框架为脑血管风险评估提供了一个实际的解决方案,支持在中风预防和监测方面的决策.
可靠的细分方便客观量化内动脉样硬化,用于早期诊断和治疗规划.

关键词:

冠状动脉冠状动脉冠状动脉脑血管疾病脑血管疾病深度学习是一种深度学习.磁共振成像技术磁共振成像技术一次性中风,中风.

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