一项对卷积神经网络的基准研究,用于大动脉根完全自动细分的大动脉根
Tingting Yang1, Guangyu Zhu1, Li Cai2
1School of Energy and Power Engineering, Xi'an Jiaotong University, Xi'an, China.
Frontiers in bioengineering and biotechnology
|July 3, 2023
概括
3D Res-UNet提供精确高效的自动大动脉根细分,用于跨导管大动脉置换 (TAVR) 手术前评估. 这种深度学习模型显著加快了细分,改善了临床工作流程并减少了潜在的并发症.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 心血管外科心血管外科
背景情况:
- 超导管大动脉置换 (TAVR) 手术前评估受益于患者特有的3D大动脉根模型.
- 传统的手动细分是耗时的,并且对临床需求无效.
- 机器学习提供自动化,准确和高效的医疗图像细分解决方案.
研究的目的:
- 量化评估四个流行的3D卷积神经网络 (CNN) 架构的细分质量和效率.
- 在TAVR手术前规划中确定最适合用于自动大动脉根细分的CNN.
主要方法:
- 在PyTorch平台上实现了四个3D CNN架构 (3D UNet,VNet,3D Res-UNet,SegResNet).
- 来自98名患者的低剂量CT血管造影 (CTA) 图像集用于培训和测试.
- 分段质量使用诸如子相似系数 (DSC),贾卡德指数和豪斯多夫距离 (HD) 等指标进行评估.
主要成果:
- 所有四个3D CNN都在回忆,DSC和Jaccard指数方面表现相似.
- 与3D UNet和SegResNet相比,3D Res-UNet表现出更高的豪斯多夫距离 (HD).
- 3D Res-UNet实现了最快的平均分段时间 (0.10 ± 0.04 秒),在效率方面明显优于其他模型.
结论:
- 3D Res-UNet在大动脉根细分方面表现出高精度和卓越的效率.
- 该模型是TAVR中自动化手术前评估的有希望的候选者.
- 使用3D Res-UNet进行更快的细分可能会简化临床工作流程并减少术后并发症.
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