Department of Radiology and Diagnostic Imaging, University of Alberta, Edmonton, Alberta, T6G 2R7, Canada.
Department of Biomedical Engineering, University of Alberta, Edmonton, Alberta, T6G 2V2, Canada; Department of Electrical Engineering, University of Alberta, Edmonton, Alberta, T6G 1H9, Canada.
Comput Biol Med. 2024 Nov;182:109174. doi: 10.1016/j.compbiomed.2024.109174. Epub 2024 Sep 24.
Individuals with malocclusion require an orthodontic diagnosis and treatment plan based on the severity of their condition. Assessing and monitoring changes in periodontal structures before, during, and after orthodontic procedures is crucial, and intraoral ultrasound (US) imaging has been shown a promising diagnostic tool in imaging periodontium. However, accurately delineating and analyzing periodontal structures in US videos is a challenging task for clinicians, as it is time-consuming and subject to interpretation errors. This paper introduces DetSegDiff, an edge-enhanced diffusion-based network developed to simultaneously detect the cementoenamel junction (CEJ) and segment alveolar bone structure in intraoral US videos. An edge feature encoder is designed to enhance edge and texture information for precise delineation of periodontal structures. Additionally, we employed the spatial squeeze-attention module (SSAM) to extract more representative features to perform both detection and segmentation tasks at global and local levels. This study used 169 videos from 17 orthodontic patients for training purposes and was subsequently tested on 41 videos from 4 additional patients. The proposed method achieved a mean distance difference of 0.17 ± 0.19 mm for the CEJ and an average Dice score of 90.1% for alveolar bone structure. As there is a lack of multi-task benchmark networks, thorough experiments were undertaken to assess and benchmark the proposed method against state-of-the-art (SOTA) detection and segmentation individual networks. The experimental results demonstrated that DetSegDiff outperformed SOTA approaches, confirming the feasibility of using automated diagnostic systems for orthodontists.
个体的牙颌畸形需要根据其严重程度进行正畸诊断和治疗计划。评估和监测正畸治疗前后牙周结构的变化至关重要，口腔内超声（US）成像已被证明是一种有前途的牙周成像诊断工具。然而，对于临床医生来说，准确地描绘和分析 US 视频中的牙周结构是一项具有挑战性的任务，因为这既耗时又容易产生解释错误。本文介绍了 DetSegDiff，这是一种基于边缘增强扩散的网络，用于同时检测口腔内 US 视频中的牙骨质-釉质界（CEJ）和牙槽骨结构。设计了一个边缘特征编码器，以增强边缘和纹理信息，从而精确描绘牙周结构。此外，我们还采用了空间挤压注意力模块（SSAM）来提取更具代表性的特征，以便在全局和局部水平上执行检测和分割任务。本研究使用了 17 名正畸患者的 169 个视频进行训练，并随后在另外 4 名患者的 41 个视频上进行了测试。该方法对 CEJ 的平均距离差异为 0.17±0.19mm，对牙槽骨结构的平均 Dice 分数为 90.1%。由于缺乏多任务基准网络，我们进行了全面的实验，以评估和基准测试该方法与最先进（SOTA）检测和分割的单个网络的性能。实验结果表明，DetSegDiff 优于 SOTA 方法，证实了为正畸医生使用自动诊断系统的可行性。
