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.

本研究解决了外部颈部吸收（ECR）在锥形束CT中的自动识别和定量评估的问题。我们提出了一种基于局部提取的体素纹理特征的自动二分类方法，能够准确检测ECR引起的微妙信号变化。此外，研究还探讨了纹理特征的聚类分析，以帮助区分缺损并识别钙化的模式，为ECR的预后生物标志物开发提供重要步骤。

基于局部纹理特征的锥形束CT外部颈部吸收自动分割