用AI精准分割颞下颌关节磁共振图像中的关节盘，提升病理诊断效率。
Segmentation of temporomandibular joint structures on mri images using neural networks for diagnosis of pathologies
- 基于自建94张影像数据集，训练并对比U-Net、YOLO等模型进行关节盘分割。
- Roboflow模型在Dice Score等指标上表现最优，适合关节盘精细分割。
- 适用于口腔科与放射科医生辅助诊断颞下颌关节疾病，推动智能化诊疗。
本文研究人工智能在颞下颌关节（TMJ）病理诊断中的应用，重点聚焦于MRI图像中关节盘的分割。由于TMJ疾病高发且传统诊断效率低，亟需更准确快速的解决方案。现有工具（Diagnocat、MandSeg）侧重骨骼结构，不适用于关节盘分析。为此，研究团队收集了94张含“颞下颌关节”和“下颌骨”类别的图像，并通过数据增强扩充数据量。随后训练并比较了U-Net、YOLOv8n、YOLOv11n及Roboflow模型，评估指标包括Dice Score、Precision、Sensitivity、Specificity和Mean Average Precision。结果表明，Roboflow模型在关节盘分割任务中表现最佳。未来计划开发测量上下颌距离及关节盘位置的算法，进一步提升病理诊断能力。
原文摘要 · Abstract (English)
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.
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