Automated Segmentation of Articular Disc of the Temporomandibular Joint in Magnetic Resonance Images Using Deep Learning: A Proof-of-Concept Study

Abstract

Temporomandibular disorders are typically accompanied by a number of clinical manifestations that involve pain and dysfunction of the masticatory muscles and temporomandibular joint. The most important subgroup of articular abnormalities in patients with temporomandibular disorders includes patients with different forms of articular disc displacement and deformation. Here, we propose a fully automated articular disc detection and segmentation system to support the diagnosis of temporomandibular disorder on magnetic resonance imaging. This system uses deep learning-based semantic segmentation approaches. Two hundred and seventeen magnetic resonance images obtained from patients with normal or displaced articular discs were used to evaluate three deep learning-based semantic segmentation approaches: our proposed encoder-decoder CNN named 3DiscNet (Detection for Displaced articular DISC using convolutional neural NETwork), U-Net, and SegNet-Basic. Of the three algorithms, 3DiscNet and SegNet-Basic showed comparably good metrics (Dice coefficient, sensitivity, and PPV). This study provides a proof-of-concept for a fully automated segmentation methodology of the articular disc on MR images with deep learning, and obtained promising initial results indicating that it could potentially be used in clinical practice for the assessment of temporomandibular disorders.