Segmentation of temporomandibular joint structures on mri images using neural networks for diagnosis of pathologies

TL;DR

This study develops and compares neural network models for segmenting TMJ structures on MRI images to improve diagnosis accuracy and speed of TMJ pathologies.

Contribution

The paper introduces a new dataset and evaluates multiple neural networks, highlighting Roboflow's effectiveness for TMJ articular disc segmentation.

Findings

Roboflow model achieved the best segmentation performance.

Data augmentation improved model accuracy.

The approach shows promise for aiding TMJ pathology diagnosis.

Abstract

This article explores the use of artificial intelligence for the diagnosis of pathologies of the temporomandibular joint (TMJ), in particular, for the segmentation of the articular disc on MRI images. The relevance of the work is due to the high prevalence of TMJ pathologies, as well as the need to improve the accuracy and speed of diagnosis in medical institutions. During the study, the existing solutions (Diagnocat, MandSeg) were analyzed, which, as a result, are not suitable for studying the articular disc due to the orientation towards bone structures. To solve the problem, an original dataset was collected from 94 images with the classes "temporomandibular joint" and "jaw". To increase the amount of data, augmentation methods were used. After that, the models of U-Net, YOLOv8n, YOLOv11n and Roboflow neural networks were trained and compared. The evaluation was carried out according to the Dice Score, Precision, Sensitivity, Specificity, and Mean Average Precision metrics. The results confirm the potential of using the Roboflow model for segmentation of the temporomandibular joint. In the future, it is planned to develop an algorithm for measuring the distance between the jaws and determining the position of the articular disc, which will improve the diagnosis of TMJ pathologies.