Reflection-equivariant convolutional neural networks improve segmentation over reflection augmentation

Abstract

Convolutional neural networks (CNNs) have been successfully applied to human brain segmentation. To in- corporate the left and right symmetry property of the brain into a network architecture, we propose a 3D left-right-reflection equivariant network to segment the anatomical structures of the brain. We extended previous group convolutions to account for left-right paired labels in the delineation. The proposed networks were compared with conventional networks trained with left-right reflection data augmentation in several tasks, showing improved performance. This is also the first work to extend reflection-equivariant CNNs to left-right paired labels in the human brain.