Individual Graph Representation Learning for Pediatric Tooth Segmentation From Dental CBCT

Abstract

Pediatric teeth exhibit significant changes in type and spatial distribution across different age groups. This variation makes pediatric teeth segmentation from cone-beam computed tomography (CBCT) more challenging than that in adult teeth. Existing methods mainly focus on adult teeth segmentation, which however cannot be adapted to spatial distribution of pediatric teeth with individual changes (SDPTIC) in different children, resulting in limited accuracy for segmenting pediatric teeth. Therefore, we introduce a novel topology structure-guided graph convolutional network (TSG-GCN) to generate dynamic graph representation of SDPTIC for improved pediatric teeth segmentation. Specifically, this network combines a 3D GCN-based decoder for teeth segmentation and a 2D decoder for dynamic adjacency matrix learning (DAML) to capture SDPTIC information for individual graph representation. 3D teeth labels are transformed into specially-designed 2D projection labels, which is accomplished by first decoupling 3D teeth labels into class-wise volumes for different teeth via one-hot encoding and then projecting them to generate instance-wise 2D projections. With such 2D labels, DAML can be trained to adaptively describe SDPTIC from CBCT with dynamic adjacency matrix, which is then incorporated into GCN for improving segmentation. To ensure inter-task consistency at the adjacency matrix level between the two decoders, a novel loss function is designed. It can address the issue with inconsistent prediction and unstable TSG-GCN convergence due to two heterogeneous decoders. The TSG-GCN approach is finally validated with both public and multi-center datasets. Experimental results demonstrate its effectiveness for pediatric teeth segmentation, with significant improvement over seven state-of-the-art methods.