使用关节细分和定位神经网络测量胸前大动脉的全自动管道
Sudeep Katakol1,2, Timothy J Baker2, Zhangxing Bian3
1University of Michigan, Department of Electrical and Computer Engineering, Ann Arbor, Michigan, United States.
Journal of medical imaging (Bellingham, Wash.)
|November 2, 2023
概括
一个新的卷积神经网络 (CNN) 算法从CT扫描中自动测量胸前大动脉动脉瘤 (TAA),减少时间和精力. 这种人工智能工具显示了与人类评分器相似的准确性,有助于TAA的诊断和监测.
科学领域:
- 医学成像分析分析 医学成像分析
- 医疗保健中的人工智能
- 心血管成像 - 心血管成像
背景情况:
- 胸腔大动脉瘤 (TAA) 诊断需要精确的大动脉直径测量,通常是通过计算机断层扫描血管学 (CTA) 手动完成的.
- 手动CTA测量是耗时的,每次扫描需要15-45分钟,阻碍了有效的临床工作流程.
- 开发自动化方法对于提高TAA评估的速度和准确性至关重要.
研究的目的:
- 开发基于卷积神经网络 (CNN) 的算法,用于完全自动和准确的大动脉直径测量.
- 简化胸前大动脉动脉瘤 (TAA) 的诊断和监测过程.
主要方法:
- 一个CNN接受了212次CTA扫描的训练,用于关节细分和大动脉的地标定位.
- 大动脉中线和截面直径是从细分面具和地标中得出的.
- 在大动脉边界上安装了一个立方线,以减轻冠状动脉起源的错误.
主要成果:
- 在大多数标准的临床地点,CNN算法实现了与手动测量相比的平均绝对误差.
- 联合CNN培训提高了细分精度,但与单独网络相比,略降低了地标本地化精度.
- 在大动脉根和中观察到较高的测量误差 (1.4-2.2毫米),手动评分器协议也较低.
结论:
- 使用基于CNN的算法,对TAA进行完全自动化的大动脉直径测量是可行的.
- 开发的算法表明了高效和准确的TAA评估的潜力.
- 为了在大动脉根和门区域的最佳性能,可能需要进一步的细化.
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