基于放射学的自动心脏细分质量控制系统:可行性研究
Qiming Liu1, Qifan Lu1, Yezi Chai1
1Department of Cardiology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200120, China.
Bioengineering (Basel, Switzerland)
|July 29, 2023
概括
本研究引入了使用深度学习和放射学进行心脏细分和质量控制 (QC) 的自动化管道. 开发的放射学方法显示了确保自动心脏细分的准确性的巨大潜力.
科学领域:
- 医疗成像医学成像
- 人工智能在医学中的应用
- 心血管成像分析心血管成像分析
背景情况:
- 自动心脏细分方法已经迅速发展,但自动质量控制 (QC) 仍然不发达.
- 确保心脏细分的准确性对于可靠的心血管分析至关重要.
研究的目的:
- 开发一个自动化管道,集成用于心脏细分的深度学习 (DL) 和用于质量控制 (QC) 的放射学.
- 解决基于DL的心脏细分方法的自动QC的缺口.
主要方法:
- 基于DL的方法用于心脏局部化和心脏亚结构 (右心室腔,心肌,左心室腔) 的细分.
- 为了特征提取,选择和开发QC模型,创建了一个放射学数据集.
- 该管道使用内部和外部测试数据集进行了评估.
主要成果:
- 细分模型实现了高的子相似系数 (DSC) 值 (例如,全心为0.954,LVC 2D为0.940,LVC 3D为0.962).
- 基于放射学的QC模型准确地预测了细分质量,平均绝对误差低 (例如,LVC 2D的0.021,LVC 3D的0.011).
- 对于二维细分QC,观察到高负检测率 (>0.85),在外部数据集上表现类似.
结论:
- 成功开发了一条用于心脏亚结构细分和2D和3D水平的QC的自动化管道.
- 放射学显示出强大的潜力,作为心脏细分质量控制的自动化方法.
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