Wang Fei, Mao Rongsong, Yan Laifa, Ling Shan, Cai Zhenyu
Center of Four-Dimensional Ultrasound, Affiliated Xiaoshan Hospital, Hangzhou Normal University, Hangzhou, Zhejiang, China.
Hangzhou Institute of Medicine, Chinese Academy of Sciences, Hangzhou, Zhejiang, China.
Front Physiol. 2023 Sep 6;14:1246994. doi: 10.3389/fphys.2023.1246994. eCollection 2023.
Diastasis recti abdominis (DRA) is a common condition in women. Measuring the distance between separated rectus abdominis (RA) in ultrasound images is a reliable method for the diagnosis of this disease. In clinical practice, the RA distance in multiple ultrasound images of a patient is measured by experienced sonographers, which is time-consuming, labor-intensive, and highly dependent on experience of operators. Therefore, an objective and fully automatic technique is highly desired to improve the DRA diagnostic efficiency. This study aimed to demonstrate the deep learning-based methods on the performance of RA segmentation and distance measurement in ultrasound images. A total of 675 RA ultrasound images were collected from 94 women, and were split into training (448 images), validation (86 images), and test (141 images) datasets. Three segmentation models including U-Net, UNet++ and Res-UNet were evaluated on their performance of RA segmentation and distance measurement. Res-UNet model outperformed the other two models with the highest Dice score (85.93% ± 0.26%), the highest MIoU score (76.00% ± 0.39%) and the lowest Hausdorff distance (21.80 ± 0.76 mm). The average physical distance between RAs measured from the segmentation masks generated by Res-UNet and that measured by experienced sonographers was only 3.44 ± 0.16 mm. In addition, these two measurements were highly correlated with each other ( = 0.944), with no systematic difference. Deep learning model Res-UNet has good reliability in RA segmentation and distance measurement in ultrasound images, with great potential in the clinical diagnosis of DRA.
腹直肌分离（DRA）是女性中的常见病症。在超声图像中测量分离的腹直肌（RA）之间的距离是诊断该疾病的可靠方法。在临床实践中，由经验丰富的超声检查人员测量患者多个超声图像中的RA距离，这既耗时、劳动强度大，又高度依赖操作人员的经验。因此，非常需要一种客观且全自动的技术来提高DRA的诊断效率。本研究旨在展示基于深度学习的方法在超声图像中RA分割和距离测量方面的性能。总共从94名女性中收集了675张RA超声图像，并将其分为训练集（448张图像）、验证集（86张图像）和测试集（141张图像）。对包括U-Net、UNet++和Res-UNet在内的三种分割模型在RA分割和距离测量方面的性能进行了评估。Res-UNet模型在Dice评分（85.93%±0.26%）、平均交并比评分（76.00%±0.39%）最高且豪斯多夫距离（21.80±0.76毫米）最低的情况下，其性能优于其他两个模型。从Res-UNet生成的分割掩码测量的RA之间的平均实际距离与经验丰富的超声检查人员测量的距离仅相差3.44±0.16毫米。此外，这两种测量结果高度相关（=0.944），且无系统差异。深度学习模型Res-UNet在超声图像中RA分割和距离测量方面具有良好的可靠性，在DRA的临床诊断中具有巨大潜力。
