Automated external cervical resorption segmentation in cone-beam CT using local texture features

2025-01-09

AI Summary

Manual assessment of external cervical root resorption (ECR) in cone-beam computed tomography (CBCT) images is time-consuming, subjective, and prone to missed diagnoses. To address this, we propose a fully automated 3D segmentation and severity quantification framework. Our method innovatively incorporates local voxel-wise texture features—specifically gray-level co-occurrence matrix (GLCM) and gray-level run-length matrix (GLRLM)—to enhance binary segmentation accuracy. Furthermore, we introduce the first unsupervised texture clustering analysis to identify intralesional calcification patterns, enabling prognostic assessment of ECR progression. Evaluated on six longitudinal CBCT datasets, the method demonstrates high sensitivity to subtle density changes induced by ECR, achieving precise lesion stratification and quantitative characterization of calcification heterogeneity. This framework significantly improves the objectivity, reproducibility, and clinical applicability of ECR quantification.

Technology Category

Computer Vision: SegmentationMachine Learning: Calibration & Uncertainty QuantificationKnowledge Representation and Reasoning: Qualitative Reasoning

Application Category

Web Mining and Content Analysis: Normalization, clustering, classification, and summarization of Web textSearch and Retrieval-Augmented AI: Web evaluation methodologies and metricsResponsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

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.

Problem

• Automated Recognition
• External Cervical Resorption
• Cone Beam Computed Tomography

Innovation

• Automated ECR Measurement
• CBCT Image Analysis
• Feature Clustering for Dental Improvement