TIPs: Tooth instance and pulp segmentation based on hierarchical extraction and fusion of anatomical priors from cone-beam CT.

Abstract

Accurate instance segmentation of tooth and pulp from cone-beam computed tomography (CBCT) images is essential but highly challenging due to the pulp's small structures and indistinct boundaries. To address these critical challenges, we propose TIPs designed for Tooth Instance and Pulp segmentation. TIPs initially employs a backbone model to segment a binary mask of the tooth from CBCT images, which is then utilized to derive position prior of the tooth and shape prior of the pulp. Subsequently, we propose the Hierarchical Fusion Mamba models to leverage the strengths of both anatomical priors and CBCT images by extracting and integrating shallow and deep features from Convolution Neural Networks (CNNs) and State Space Sequence Models (SSMs), respectively. This process achieves tooth instance and pulp segmentation, which are then combined to obtain the final pulp instance segmentation. Extensive experiments on CBCT scans from 147 patients demonstrate that TIPs significantly outperforms state-of-the-art methods in terms of segmentation accuracy. Furthermore, we have encapsulated this framework into an openly accessible tool for one-click using. To our knowledge, this is the first toolbox capable of segmentation of tooth and pulp instances, with its performance validated on two external datasets comprising 59 samples from the Toothfairy2 dataset and 48 samples from the STS dataset. These results demonstrate the potential of TIPs as a practical tool to boost clinical workflows in digital dentistry, enhancing the precision and efficiency of dental diagnostics and treatment planning.