Shape-preserving Tooth Segmentation from CBCT Images Using Deep Learning with Semantic and Shape Awareness

TL;DR

This paper presents a deep learning framework for accurate, shape-preserving tooth segmentation from CBCT images, effectively handling anatomical distortions with semantic and shape-aware mechanisms.

Contribution

It introduces a novel multi-label and shape-aware learning approach that jointly optimizes segmentation accuracy and morphological fidelity, addressing shape distortion challenges.

Findings

Outperforms existing methods on multiple datasets

Effectively preserves tooth boundary integrity

Reduces shape ambiguity in complex cases

Abstract

Background:Accurate tooth segmentation from cone beam computed tomography (CBCT) images is crucial for digital dentistry but remains challenging in cases of interdental adhesions, which cause severe anatomical shape distortion. Methods: To address this, we propose a deep learning framework that integrates semantic and shape awareness for shape-preserving segmentation. Our method introduces a target-tooth-centroid prompted multi-label learning strategy to model semantic relationships between teeth, reducing shape ambiguity. Additionally, a tooth-shape-aware learning mechanism explicitly enforces morphological constraints to preserve boundary integrity. These components are unified via multi-task learning, jointly optimizing segmentation and shape preservation. Results: Extensive evaluations on internal and external datasets demonstrate that our approach significantly outperforms existing methods. Conclusions: Our approach effectively mitigates shape distortions and providing anatomically faithful tooth boundaries.