用局部纹理特征自动分割锥形束CT中的牙颈部外吸收病灶
Automated external cervical resorption segmentation in cone-beam CT using local texture features
- 基于体素级纹理特征的二分类方法实现病灶自动识别
- 在6个纵向数据集上准确捕捉细微的CBCT信号变化
- 可初步区分钙化模式，助力预后判断与治疗决策
牙颈部外吸收（ECR）是一种影响牙齿的吸收性病变。部分患者中，活动性吸收会停止并被骨组织替代；而在另一些病例中，吸收持续进展，最终导致牙齿脱落。为准确评估ECR，推荐使用锥形束计算机断层扫描（CBCT），其可实现病变的三维表征。尽管可手动识别和测量CBCT中的ECR，但该过程耗时且易受人为误差影响。因此亟需开发一种基于CBCT的自动化方法，以识别并量化ECR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分类的ECR病灶分割方法。我们在6个纵向CBCT数据集上评估该方法，发现特定纹理特征可有效检测由ECR引起的细微CBCT信号变化。此外，我们还进行了初步分析，通过聚类病灶内纹理特征对缺陷进行分层，并识别出提示钙化的模式。这些方法是开发预测ECR是否将持续进展或停止的预后生物标志物的重要步骤，最终有助于指导治疗决策。
原文摘要 · Abstract (English)
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.
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