Dental pathology detection in 3D cone-beam CT
Cone-beam computed tomography (CBCT) is a valuable imaging method in dental diagnostics that provides information not available in traditional 2D imaging. However, interpretation of CBCT images is a time-consuming process that requires a physician to work with complicated software. In this work we propose an automated pipeline composed of several deep convolutional neural networks and algorithmic heuristics. Our task is two-fold: a) find locations of each present tooth inside a 3D image volume, and b) detect several common tooth conditions in each tooth. The proposed system achieves 96.3\% accuracy in tooth localization and an average of 0.94 AUROC for 6 common tooth conditions.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Assessment of Deep-Learning Methods for the Enhancement of Experimental Low Dose Dental CBCT Volumes
Cone-beam tomography enables rapid 3D acquisitions, making it a suitable imaging modality for dental imaging. However, as with all X-ray techniques, the main challenge is to reduce the dose while maintaining good image q…
An efficient method to automate tooth identification and 3D bounding box extraction from Cone Beam CT Images
Accurate identification, localization, and segregation of teeth from Cone Beam Computed Tomography (CBCT) images are essential for analyzing dental pathologies. Modeling an individual tooth can be challenging and intrica…
Gradient Step Plug-and-Play Model for Dental Cone-Beam CT Reconstruction
The goal of this work is to reduce the effect of photon noise in dental cone-beam CT reconstruction. We consider an inverse problem formulation and develop a databased prior. To this end, we simulate fan-beam acquisition…
Nonlinear ill-posed problem in low-dose dental cone-beam computed tomography
This paper describes the mathematical structure of the ill-posed nonlinear inverse problem of low-dose dental cone-beam computed tomography (CBCT) and explains the advantages of a deep learning-based approach to the reco…
Image ReconstructionA fully automatic AI system for tooth and alveolar bone segmentation from cone-beam CT images
Accurate delineation of individual teeth and alveolar bones from dental cone-beam CT (CBCT) images is an essential step in digital dentistry for precision dental healthcare. In this paper, we present an AI system for eff…
Segmentation