Nabla-net: A deep dag-like convolutional architecture for biomedical image segmentation

Abstract

Biomedical image segmentation requires both voxel-level information and global context. We report on a deep convolutional architecture which combines a fully-convolutional network for local features and an encoder-decoder network in which convolutional layers and maxpooling compute high-level features, which are then upsampled to the resolution of the initial image using further convolutional layers and tied unpooling. We apply the method to segmenting multiple sclerosis lesions and gliomas.