Automatic segmentation of extensor carpi ulnaris tendon and detection of tendinosis with convolutional neural networks.

Abstract

Background: Extensor Carpi Ulnaris (ECU) tendinosis, a frequent cause of chronic wrist pain, requires prompt diagnosis to prevent disability. This study demonstrates the use of convolutional neural networks (CNNs) for automated detection and segmentation of the ECU tendon and tendinosis in 2D axial wrist MRI.

Purpose: To develop a CNN for the automated detection of ECU tendon and automatic delineation of tendinosis in 2D wrist MRI. The study serves as a proof-of-concept, demonstrating the feasibility of automating the segmentation of musculoskeletal structures in wrist MRI and offering an efficient solution for detecting tendinosis.

Material and methods: In a retrospective analysis of 1081 patients undergoing wrist MRI imaging, 46 patients exhibited tendinosis. Two deep learning-based methods for segmenting the ECU tendon and T2 hyperintense lesions indicative of tendinosis from 2D axial wrist MRI series were developed and compared in this study. Both methods were trained and evaluated over all 46 patients using Dice score as the main evaluation metric.

Results: The mean ECU tendon segmentation Dice score ranged from 0.61 to 0.64 (± 0.27 to 0.31). Tendinosis detection yielded a Dice score of 0.38 for both the threshold method (±0.19) and the CNN (±0.22). A Dice score > 0.50 indicated successful detection, with our methods achieving a detection rate of 72-76%.

Conclusion: The developed CNN effectively detected and segmented the ECU tendon in 2D MRI series. Tendinosis was detected with comparable accuracy using both signal intensity thresholding and the trained CNN method.