Hierarchical Multi-Class Group Correlation Learning Network for Medical Image Segmentation

Hierarchical approaches have been tremendously successful at multi-label segmentation. However, it has been shown they may seriously suffer from the problem of only imposing constraints on shallow layers while ignoring deep relationships in the label space. In this paper we overcome this limitation through a hierarchical multi-class group correlation learning (HMGC). Thus, we first transform regional constraints into voxel vector correlations in a high-dimensional space. After performing transformation, we compute a voxel vector correlation matrix to group voxel vectors to reduce disparities between erroneous and valid vectors. We then introduce two loss functions: intra-class group loss, which minimizes differences within the same class, and inter-class group loss, which adjusts distances between class group centers and voxel vectors. This, in turn, can be used to mitigate bias propagation and improve segmentation accuracy. The effectiveness of our method is demonstrated on three Brain Tumor Segmentation Challenge datasets: BraTS2018, BraTS2019, and BraTS2020. Moreover, generalization of our method is evaluated on the ACDC MICCAI'17 Challenge Dataset. Our HMGC model ranks first in overall score on Brats2020 and achieves one of the most competitive results in cardiac segmentation.