Wu Chueh-Hung, Syu Wei-Ting, Lin Meng-Ting, Yeh Cheng-Liang, Boudier-Revéret Mathieu, Hsiao Ming-Yen, Kuo Po-Ling
Department of Physical Medicine and Rehabilitation, National Taiwan University Hospital Hsin-Chu Branch, Hsinchu 300, Taiwan.
Department of Physical Medicine and Rehabilitation, National Taiwan University Hospital, College of Medicine, National Taiwan University, Taipei 100, Taiwan.
Diagnostics (Basel). 2021 Oct 14;11(10):1893. doi: 10.3390/diagnostics11101893.
There is an emerging trend to employ dynamic sonography in the diagnosis of entrapment neuropathy, which exhibits aberrant spatiotemporal characteristics of the entrapped nerve when adjacent tissues move. However, the manual tracking of the entrapped nerve in consecutive images demands tons of human labors and impedes its popularity clinically. Here we evaluated the performance of automated median nerve segmentation in dynamic sonography using a variety of deep learning models pretrained with ImageNet, including DeepLabV3+, U-Net, FPN, and Mask-R-CNN. Dynamic ultrasound images of the median nerve at across wrist level were acquired from 52 subjects diagnosed as carpal tunnel syndrome when they moved their fingers. The videos of 16 subjects exhibiting diverse appearance and that of the remaining 36 subjects were used for model test and training, respectively. The centroid, circularity, perimeter, and cross section area of the median nerve in individual frame were automatically determined from the inferred nerve. The model performance was evaluated by the score of intersection over union (IoU) between the annotated and model-predicted data. We found that both DeepLabV3+ and Mask R-CNN predicted median nerve the best with averaged IOU scores close to 0.83, which indicates the feasibility of automated median nerve segmentation in dynamic sonography using deep learning.
在诊断卡压性神经病时，采用动态超声检查正成为一种新趋势，当相邻组织移动时，这种检查可显示出被卡压神经异常的时空特征。然而，在连续图像中手动追踪被卡压神经需要大量人力，阻碍了其在临床上的广泛应用。在此，我们使用多种在ImageNet上预训练的深度学习模型，包括DeepLabV3 +、U-Net、FPN和Mask-R-CNN，评估了动态超声检查中正中神经自动分割的性能。从52名被诊断为腕管综合征的受试者在移动手指时获取腕部水平正中神经的动态超声图像。分别将16名外观多样的受试者的视频和其余36名受试者的视频用于模型测试和训练。根据推断出的神经自动确定各帧中正中神经的质心、圆形度、周长和横截面积。通过标注数据与模型预测数据之间的交并比（IoU）分数评估模型性能。我们发现，DeepLabV3 +和Mask R-CNN对正中神经的预测效果最佳，平均IoU分数接近0.83，这表明使用深度学习在动态超声检查中进行正中神经自动分割是可行的。
