External cervical resorption (ECR) is a resorptive process affecting teeth. While in some patients, active resorption ceases and gets replaced by osseous tissue, in other cases, the resorption progresses and ultimately results in tooth loss. For proper ECR assessment, cone-beam computed tomography (CBCT) is the recommended imaging modality, enabling a 3-D characterization of these lesions. While it is possible to manually identify and measure ECR resorption in CBCT scans, this process can be time intensive and highly subject to human error. Therefore, there is an urgent need to develop an automated method to identify and quantify the severity of ECR resorption using CBCT. Here, we present a method for ECR lesion segmentation that is based on automatic, binary classification of locally extracted voxel-wise texture features. We evaluate our method on 6 longitudinal CBCT datasets and show that certain texture-features can be used to accurately detect subtle CBCT signal changes due to ECR. We also present preliminary analyses clustering texture features within a lesion to stratify the defects and identify patterns indicative of calcification. These methods are important steps in developing prognostic biomarkers to predict whether ECR will continue to progress or cease, ultimately informing treatment decisions.

翻译：牙颈部外吸收是一种影响牙齿的吸收性病变。部分患者的活动性吸收过程会停止并被骨组织替代，而另一些病例中吸收过程持续进展并最终导致牙齿丧失。为准确评估牙颈部外吸收，锥形束计算机断层扫描是推荐的影像学检查手段，能够实现对这些病变的三维表征。虽然可以手动识别和测量锥形束CT扫描中的牙颈部外吸收区域，但该过程耗时且极易受人为主观误差影响。因此，亟需开发基于锥形束CT的自动方法来识别和量化牙颈部外吸收的严重程度。本文提出一种基于局部提取的体素级纹理特征进行自动二值分类的牙颈部外吸收病变分割方法。我们在6个纵向锥形束CT数据集上评估了该方法，结果表明特定纹理特征能够准确检测由牙颈部外吸收引起的细微锥形束CT信号变化。我们还提出了对病变内部纹理特征进行聚类分析的初步研究，以分层识别缺损区域并检测指示钙化的特征模式。这些方法是开发预后生物标志物的重要步骤，可用于预测牙颈部外吸收将持续进展还是终止，最终为临床治疗决策提供依据。