Deep Hierarchy-Aware Segmentation: A Novel Framework for MRIs Brain Tumor Segmentation

The exploitation of label hierarchy is crucial for effective brain tumor segmentation. Nevertheless, existing methods grapple with two key limitations. First, they lack the hierarchical dependency of predictions across different label levels, rendering the network outputs less interpretable. Second, they fail to exploit the hierarchal similarity among labels, thus hindering potential accuracy enhancement. To address these limitations, we present a novel framework termed deep hierarchy-aware segmentation (DHAS), which achieves both hierarchically interpretable and high-accuracy predictions. Specifically, to generate hierarchical predictions, the network is designed to output pixel-wise probability conditional upon the parent label and is hybrid-trained from conditional to unconditional probability. To utilize the label similarity, we propose a tree-triplet loss, which imposes the hierarchy-induced distance within the feature embedding space. Experimental results on three datasets, BraTS2018, BraTS2019 and BraTS2020, show that our proposed framework achieves significantly better performance than other hierarchy-exploiting methods, and it ranks fifth top among 383 participating methods in Brats2020 Challenge. The improved performance and interpretable predictions promise the potential of DHAS for clinical applications in brain tumor segmentation. Furthermore, its generalization is demonstrated for cardiac segmentation on ACDC dataset.