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

TL;DR

This paper introduces an automated method for segmenting external cervical resorption in CBCT scans using local texture features, aiming to improve accuracy and reduce human error in diagnosis.

Contribution

It presents a novel voxel-wise texture feature classification approach for ECR segmentation in CBCT images, with evaluation on multiple datasets.

Findings

Texture features can accurately detect subtle ECR signals.

The method can cluster lesion textures to identify patterns.

Preliminary analysis suggests potential for prognostic biomarker development.

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.