Hämäläinen Mathias, Sormaala Markus, Kaseva Tuomas, Salli Eero, Savolainen Sauli, Kangasniemi Marko
Department of Radiology, HUS Diagnostic Center, Helsinki University Hospital and University of Helsinki, and Jorvi Hospital, Espoo, Finland.
Department of Physics, University of Helsinki, Helsinki, Finland.
Acta Radiol Open. 2024 Nov 30;13(11):20584601241297530. doi: 10.1177/20584601241297530. eCollection 2024 Nov.
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.
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.
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.
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%.
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.
尺侧腕伸肌（ECU）肌腱病是慢性腕部疼痛的常见原因，需要及时诊断以防止残疾。本研究展示了卷积神经网络（CNN）在二维轴向腕部MRI中对ECU肌腱和肌腱病进行自动检测和分割的应用。
开发一种用于在二维腕部MRI中自动检测ECU肌腱并自动勾勒肌腱病的CNN。该研究作为概念验证，证明了在腕部MRI中自动分割肌肉骨骼结构的可行性，并为检测肌腱病提供了一种有效的解决方案。
在对1081例接受腕部MRI成像的患者进行的回顾性分析中，46例表现出肌腱病。本研究开发并比较了两种基于深度学习的方法，用于从二维轴向腕部MRI序列中分割ECU肌腱和指示肌腱病的T2高信号病变。两种方法均在所有46例患者中进行训练和评估，以Dice分数作为主要评估指标。
ECU肌腱分割的平均Dice分数在0.61至0.64之间（±0.27至0.31）。阈值法（±0.19）和CNN（±0.22）检测肌腱病的Dice分数均为0.38。Dice分数>0.50表示检测成功，我们的方法检测率达到72-76%。
所开发的CNN有效地在二维MRI序列中检测和分割了ECU肌腱。使用信号强度阈值法和经过训练的CNN方法检测肌腱病的准确性相当。
