A Stagewise Deep Learning Framework for Tooth Instance Segmentation in CBCT Images

Computer-assisted modeling of patient-specific 3D teeth is a clinically important technology for the development of dental diagnosis and treatment. This technology often relies on accurately segmenting the target tooth and its surrounding tissues from CBCT images. Most of the previous methods consume extensive memory for generating bounding box proposals in a detection manner, while in this paper, we propose a novel stagewise tooth instance segmentation framework from localization to segmentation. Specifically, our method follows the process of tooth centroid prediction, candidate centroid analysis, and mapping of centroids to accurately localize the ROI of individual teeth, instead of generating bounding box proposals for tooth positioning regression. To improve the segmentation quality, we propose a new loss function referred to as potential energy loss, which measures the feature similarity among voxels in a neighborhood to focus more on local information, regulating potential energy to obtain optimal segmentation. Moreover, the proposed fine segmentation network introduces a dual-branch structure and spectrum filter connections to enhance hierarchical features and anti-noise capability. Experimental results demonstrate that the proposed method surpasses state-of-the-art methods with improvements of 1.05%, 5.77%, and 16.67% on average DSC, HD95, and ASSD, respectively.