Learning Ontology-Based Hierarchical Structural Relationship for Whole Brain Segmentation

Abstract

Whole brain segmentation is vital for a variety of anatomical investigations in brain development, aging, and degradation. It is nevertheless challenging to accurately segment fine-grained brain structures due to the low soft-tissue contrast. In this work, we propose and validate a novel method for whole brain segmentation. By learning ontology-based hierarchical structural knowledge with a triplet loss enhanced by graph-based dynamic violate margin, our method can mimic experts’ hierarchical perception of the brain anatomy and capture the relationship across different structures. We evaluate the whole brain segmentation performance of our method on two publicly-accessible datasets, namely JHU Adult Atlas and CANDI, respectively possessing fine-grained (282) and coarse-grained (32) manual labels. Our method achieves mean Dice similarity coefficients of 83.67% and 88.23% on the two datasets. Quantitative and qualitative results identify the superiority of the proposed method over representative state-of-the-art whole brain segmentation approaches. The code is available at https://github.com/CRazorback/OHSR.