一种基于深度学习的方法,用于腹直体细分和超声波中距离测量
Fei Wang1, Rongsong Mao2, Laifa Yan2
1Center of Four-Dimensional Ultrasound, Affiliated Xiaoshan Hospital, Hangzhou Normal University, Hangzhou, Zhejiang, China.
Frontiers in physiology
|September 22, 2023
概括
一个深度学习模型,Res-UNet,准确地细分直肠腹部 (RA),并在超声波图像中测量直肠腹部 (DRA). 这种自动化方法显示出高可靠性和与专家测量结果的相关性,提高了产后妇女的诊断效率.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 超声波技术 超声波技术 超声波技术
背景情况:
- 腹部直流 (DRA) 在产后妇女中很常见.
- 在超声波图像中手动测量直肠腹部 (RA) 分离是耗时且取决于操作者.
- 需要一种自动化,客观的DRA诊断方法.
研究的目的:
- 评估深度学习模型,用于自动化RA细分和超声波图像中的距离测量.
- 为了比较U-Net,UNet++和Res-UNet模型对DRA诊断的性能.
- 评估基于深度学习的测量的可靠性,与经验丰富的超声波仪器对比.
主要方法:
- 收集了来自94名产后妇女的675张RA超声波图像.
- 经过训练和验证的U-Net,UNet++和Res-UNet模型用于RA细分.
- 通过使用子得分,MIoU和豪斯多夫距离来评估细分性能.
- 将自动距离测量与经验丰富的超声波测量器进行了比较.
主要成果:
- Res-UNet获得了最高的子得分 (85.93%) 和MIoU (76.00%),以及最低的豪斯多夫距离 (21.80毫米).
- Res-UNet测量与超声波测量密切相关 (r=0.944),平均差异很小 (3.44毫米).
- 在自动测量和专家测量之间没有观察到系统差异.
结论:
- Res-UNet深度学习模型在超声波图像中对RA细分和距离测量具有高可靠性.
- 这种自动化方法具有显著的潜力,可以提高DRA临床诊断的效率和客观性.
- 深度学习为改善产后护理和DRA管理提供了一个有前途的工具.
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