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基于深度学习的图像细分方法的比较,用于回顾性和大图像队列研究中的血管内超声波

Liang Dong¹, Wei Lu², Xuzhou Lu²

¹The Department of Cardiology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China.

Biomedical engineering online

|November 29, 2023

概括

深度学习模型在细分血管内超声波 (IVUS) 图像,用于光膜和外部弹性膜 (EEM) 中表现出强的性能. 自动测量与手动测量密切一致,支持临床IVUS评估.

科学领域:

医疗成像医学成像
人工智能的人工智能
心脏病学心脏病学

背景情况:

血管内超声波 (IVUS) 对于评估冠状动脉疾病至关重要.
精确的细分光膜和外部弹性膜 (EEM) 对于定量IVUS分析至关重要.
深度学习模型为自动化IVUS图像分析提供了潜力.

研究的目的:

在大型IVUS数据集上评估深度学习细分模型的性能.
评估自动化IVUS定量测量的准确性和一致性.
为了比较IVUS图像的不同最先进的细分模型.

主要方法:

在113名患者的1,070张IVUS图像上训练并测试深度学习模型.
通过使用子相似系数 (DSC),过联交点 (IoU) 和豪斯多夫距离 (HD) 的五种细分模型进行了比较.
使用类内相关系数 (ICC) 和布兰德-阿尔特曼图表的自动和手动定量测量之间的评估一致性.

主要成果:

在光量和EME细分方面,CENET获得了最高的DSC和IOU.
Res-UNet在豪斯多夫距离中表现最好.
自动测量显示与手动测量有很强的一致性 (ICC = 0.855).

结论:

深度学习模型在大型IVUS数据集上实现了最先进的细分.
使用深度学习的自动化IVUS分析显示与临床测量有很好的一致性.
这些模型适用于临床IVUS评估和定量分析.

关键词:

化斑块是一种化斑块.深度学习是一种深度学习.图像细分图像细分图像细分血管内超声波血管内超声波

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