Deep learning for the rapid automatic segmentation of forearm muscle boundaries from ultrasound datasets

Chen Xin; Baoxu Li; Dezheng Wang; Wei Chen; Shouwei Yue; Dong Meng; Xu Qiao; Yang Zhang

doi:10.3389/fphys.2023.1166061

Deep learning for the rapid automatic segmentation of forearm muscle boundaries from ultrasound datasets

Front Physiol. 2023 Jul 13:14:1166061. doi: 10.3389/fphys.2023.1166061. eCollection 2023.

Authors

Chen Xin¹, Baoxu Li², Dezheng Wang¹, Wei Chen³, Shouwei Yue¹, Dong Meng¹, Xu Qiao³, Yang Zhang¹

Affiliations

¹ Rehabilitation Center, Qilu Hospital of Shandong University, Jinan, China.
² School of Mathematics, Shandong University, Jinan, China.
³ Department of Biomedical Engineering, School of Control Science and Engineering, Shandong University, Jinan, Shandong, China.

Abstract

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.

Keywords: U-net; deep learning; image recognition; muscle boundaries; ultrasound.

Grants and funding

The project is supported in part by the Natural Science Foundation of Shandong Province of China (No. ZR202102200383), in part by the Program for Youth Innovative Research Team in the University of Shandong Province in China (No. 2019KJN010), in part by the National Natural Science Foundation of China (No. 82172535), in part by the Science and Technology Innovation Project of Shandong Province (No.2019JZZY011112), and in part by the Fundamental Research Funds for Central Universities (No. 2022JC013).