Brosset Serge, Dumont Maxime, Bianchi Jonas, Ruellas Antonio, Cevidanes Lucia, Yatabe Marilia, Goncalves Joao, Benavides Erika, Soki Fabiana, Paniagua Beatriz, Prieto Juan, Najarian Kayvan, Gryak Jonathan, Soroushmehr Reza
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:1270-1273. doi: 10.1109/EMBC44109.2020.9175692.
Temporomandibular joints (TMJ) like a hinge connect the jawbone to the skull. TMJ disorders could cause pain in the jaw joint and the muscles controlling jaw movement. However, the disease cannot be diagnosed until it becomes symptomatic. It has been shown that bone resorption at the condyle articular surface is already evident at initial diagnosis of TMJ Osteoarthritis (OA). Therefore, analyzing the bone structure will facilitate the disease diagnosis. The important step towards this analysis is the condyle segmentation. This article deals with a method to automatically segment the temporomandibular joint condyle out of cone beam CT (CBCT) scans. In the proposed method we denoise images and apply 3D active contour and morphological operations to segment the condyle. The experimental results show that the proposed method yields the Dice score of 0.9461 with the standards deviation of 0.0888 when it is applied on CBCT images of 95 patients. This segmentation will allow large datasets to be analyzed more efficiently towards data sciences and machine learning approaches for disease classification.
颞下颌关节（TMJ）如同一个铰链，将颌骨与颅骨相连。颞下颌关节紊乱会导致颌关节以及控制颌骨运动的肌肉疼痛。然而，这种疾病在出现症状之前无法被诊断出来。研究表明，在颞下颌关节骨关节炎（OA）初诊时，髁突关节表面的骨质吸收就已经很明显了。因此，分析骨骼结构将有助于疾病的诊断。进行这种分析的重要一步是髁突分割。本文介绍了一种从锥形束CT（CBCT）扫描中自动分割颞下颌关节髁突的方法。在所提出的方法中，我们对图像进行去噪，并应用三维活动轮廓和形态学操作来分割髁突。实验结果表明，当该方法应用于95名患者的CBCT图像时，其Dice系数为0.9461，标准差为0.0888。这种分割将使我们能够更有效地分析大型数据集，以用于疾病分类的数据科学和机器学习方法。
