Xin Chen, Li Baoxu, Wang Dezheng, Chen Wei, Yue Shouwei, Meng Dong, Qiao Xu, Zhang Yang
Rehabilitation Center, Qilu Hospital of Shandong University, Jinan, China.
School of Mathematics, Shandong University, Jinan, China.
Front Physiol. 2023 Jul 13;14:1166061. doi: 10.3389/fphys.2023.1166061. eCollection 2023.
Ultrasound (US) is widely used in the clinical diagnosis and treatment of musculoskeletal diseases. However, the low efficiency and non-uniformity of artificial recognition hinder the application and popularization of US for this purpose. Herein, we developed an automatic muscle boundary segmentation tool for US image recognition and tested its accuracy and clinical applicability. Our dataset was constructed from a total of 465 US images of the flexor digitorum superficialis (FDS) from 19 participants (10 men and 9 women, age 27.4 ± 6.3 years). We used the U-net model for US image segmentation. The U-net output often includes several disconnected regions. Anatomically, the target muscle usually only has one connected region. Based on this principle, we designed an algorithm written in C++ to eliminate redundantly connected regions of outputs. The muscle boundary images generated by the tool were compared with those obtained by professionals and junior physicians to analyze their accuracy and clinical applicability. The dataset was divided into five groups for experimentation, and the average Dice coefficient, recall, and accuracy, as well as the intersection over union (IoU) of the prediction set in each group were all about 90%. Furthermore, we propose a new standard to judge the segmentation results. Under this standard, 99% of the total 150 predicted images by U-net are excellent, which is very close to the segmentation result obtained by professional doctors. In this study, we developed an automatic muscle segmentation tool for US-guided muscle injections. The accuracy of the recognition of the muscle boundary was similar to that of manual labeling by a specialist sonographer, providing a reliable auxiliary tool for clinicians to shorten the US learning cycle, reduce the clinical workload, and improve injection safety.
超声（US）广泛应用于肌肉骨骼疾病的临床诊断和治疗。然而，人工识别的低效率和不一致性阻碍了超声在此方面的应用和推广。在此，我们开发了一种用于超声图像识别的自动肌肉边界分割工具，并测试了其准确性和临床适用性。我们的数据集由19名参与者（10名男性和9名女性，年龄27.4±6.3岁）的总共465张指浅屈肌（FDS）的超声图像构建而成。我们使用U-net模型进行超声图像分割。U-net输出通常包括几个不相连的区域。从解剖学角度来看，目标肌肉通常只有一个相连区域。基于这一原理，我们设计了一种用C++编写的算法来消除输出中的冗余相连区域。将该工具生成的肌肉边界图像与专业人员和初级医生获得的图像进行比较，以分析其准确性和临床适用性。将数据集分为五组进行实验，每组预测集的平均Dice系数、召回率、准确率以及交并比（IoU）均约为90%。此外，我们提出了一种判断分割结果的新标准。在该标准下，U-net预测的150张图像中99%都非常出色，这与专业医生获得的分割结果非常接近。在本研究中，我们开发了一种用于超声引导肌肉注射的自动肌肉分割工具。肌肉边界识别的准确性与专业超声医师手动标注的准确性相似，为临床医生缩短超声学习周期、减轻临床工作量并提高注射安全性提供了可靠的辅助工具。
