Segmentation of finger tendon and synovial sheath in ultrasound image using deep convolutional neural network

Abstract

Background: Trigger finger is a common hand disease, which is caused by a mismatch in diameter between the tendon and the pulley. Ultrasound images are typically used to diagnose this disease, which are also used to guide surgical treatment. However, background noise and unclear tissue boundaries in the images increase the difficulty of the process. To overcome these problems, a computer-aided tool for the identification of finger tissue is needed. Results: Two datasets were used for evaluation: one comprised different cases of individual images and another consisting of eight groups of continuous images. Regarding result similarity and contour smoothness, our proposed deeply supervised dilated fully convolutional DenseNet (D2FC-DN) is better than ATASM (the state-of-art segmentation method) and representative CNN methods. As a practical application, our proposed method can be used to build a tendon and synovial sheath model that can be used in a training system for ultrasound-guided trigger finger surgery. Conclusion: We proposed a D2FC-DN for finger tendon and synovial sheath segmentation in ultrasound images. The segmentation results were remarkably accurate for two datasets. It can be applied to assist the diagnosis of trigger finger by highlighting the tissues and generate models for surgical training systems in the future. Methods: We propose a novel finger tendon segmentation method for use with ultrasound images that can also be used for synovial sheath segmentation that yields a more complete description for analysis. In this study, a hybrid of effective convolutional neural network techniques are applied, resulting in a deeply supervised dilated fully convolutional DenseNet (D2FC-DN), which displayed excellent segmentation performance on the tendon and synovial sheath.

Artificial intelligence in patient-specific hand surgery: a scoping review of literature

“…Kuok et al developed a computer-aided soft tissue segmentation method using a deep convolutional neural network , to help distinguish tendon tissue from synovial sheath tissue in ultrasound images. The authors suggested the potential use of the method in diagnosis and ultrasound-guide treatment of trigger finger [ 22 ].…”

Applications of artificial intelligence in musculoskeletal ultrasound: narrative review

“…There are multiple clinical settings in which application of ML models can aid MSUS. These include the assessment of synovial tissue (15,18), tendon (19,20), cartilage (21) and nerve identification (11,22,23). When examining these structures, a ML algorithm can perform either recognition or a diagnostic task.…”