Biochemical analysis of the articular disc of the temporomandibular joint with magnetic resonance T2 mapping: a feasibility study

2013 ◽  
Vol 18 (7) ◽  
pp. 1865-1871 ◽  
Author(s):  
Martina Schmid-Schwap ◽  
Margit Bristela ◽  
Elisabeth Pittschieler ◽  
Astrid Skolka ◽  
Pavol Szomolanyi ◽  
...  
Keyword(s):  
Magnetic Resonance ◽  
Temporomandibular Joint ◽  
Feasibility Study ◽  
Biochemical Analysis ◽  
T2 Mapping ◽  
Articular Disc

scholarly journals Automated segmentation of articular disc of the temporomandibular joint on magnetic resonance images using deep learning

2022 ◽  
Vol 12 (1) ◽  
Author(s):  
Shota Ito ◽  
Yuichi Mine ◽  
Yuki Yoshimi ◽  
Saori Takeda ◽  
Akari Tanaka ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Magnetic Resonance ◽  
Convolutional Neural Network ◽  
Temporomandibular Joint ◽  
Temporomandibular Disorders ◽  
Temporomandibular Disorder ◽  
Semantic Segmentation ◽  
Magnetic Resonance Images ◽  
Articular Disc

AbstractTemporomandibular 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. The study included a total of 217 magnetic resonance images from 10 patients with anterior displacement of the articular disc and 10 healthy control subjects with normal articular discs. These images were used to evaluate three deep learning-based semantic segmentation approaches: our proposed convolutional neural network encoder-decoder 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 positive predictive value). This study provides a proof-of-concept for a fully automated deep learning-based segmentation methodology for articular discs on magnetic resonance images, and obtained promising initial results, indicating that the method could potentially be used in clinical practice for the assessment of temporomandibular disorders.

scholarly journals Evaluation of TMJ articular eminence morphology and disc patterns in patients with disc displacement in MRI

2007 ◽  
Vol 21 (3) ◽  
pp. 265-271 ◽  
Author(s):  
Fabio Henrique Hirata ◽  
Antônio Sérgio Guimarães ◽  
Jefferson Xavier de Oliveira ◽  
Carla Ruffeil Moreira ◽  
Evangelo Tadeu Terra Ferreira ◽  
...  
Keyword(s):  
Magnetic Resonance ◽  
Temporomandibular Joint ◽  
Contralateral Side ◽  
Magnetic Resonance Images ◽  
Disc Displacement ◽  
Articular Disc ◽  
Articular Eminence ◽  
Disc Position ◽  
Disc Configuration ◽  
Disc Displacement With Reduction

The aim of this study was to assess the shape of the temporomandibular joint (TMJ) articular eminence and the articular disc configuration and position in patients with disc displacement. TMJ magnetic resonance images (MRI) of 14 patients with bilateral disc displacement without unilateral reduction were analyzed. Articular eminence morphology was characterized as box, sigmoid, flattened, or deformed. Articular disc configuration was divided into biconcave, biplanar, biconvex, hemiconvex or folded, and its position, as "a" (superior), "b" (anterosuperior), "c" (anterior) or "d" (anteroinferior). The images were divided and the sides with disc displacement with reduction (DDWR) and without reduction (DDWOR) were compared. Regarding articular eminence shape, the sigmoid form presented the greatest incidence, followed by the box form, in the DDWR side, although this was not statistically significant. In the DDWOR side, the flattened shape was the most frequent (p = 0.041). As to disc configuration, the biconcave shape was found in 79% of the DDWR cases (p = 0.001) and the folded type predominated in 43% of the DDWOR cases (p = 0.008). As to disc position, in the DDWR side, "b" (anterosuperior position) was the most frequent (p = 0.001), whereas in the DDWOR side, "d" (anteroinferior position) was the most often observed (p = 0.001). The side of the patient with altered disc configuration and smaller shape of TMJ articular eminence seems to be more likely to develop non-reducing disc displacement as compared to the contralateral side.

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

2021 ◽  
Author(s):  
Shota Ito ◽  
Yuichi Mine ◽  
Yuki Yoshimi ◽  
Saori Takeda ◽  
Akari Tanaka ◽  
...  
Keyword(s):  
Deep Learning ◽  
Magnetic Resonance ◽  
Temporomandibular Joint ◽  
Temporomandibular Disorders ◽  
Temporomandibular Disorder ◽  
Semantic Segmentation ◽  
Magnetic Resonance Images ◽  
Automated Segmentation ◽  
Proof Of Concept ◽  
Articular Disc

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.

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