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Recently deep residual learning with residual units for training very deepneural networks advanced the state-of-the-art performance on 2D imagerecognition tasks, e.g., object detection and segmentation. However, how tofully leverage contextual representations for recognition tasks from volumetricdata has not been well studied, especially in the field of medical imagecomputing, where a majority of image modalities are in volumetric format. Inthis paper we explore the deep residual learning on the task of volumetricbrain segmentation. There are at least two main contributions in our work.First, we propose a deep voxelwise residual network, referred as VoxResNet,which borrows the spirit of deep residual learning in 2D image recognitiontasks, and is extended into a 3D variant for handling volumetric data. Second,an auto-context version of VoxResNet is proposed by seamlessly integrating thelow-level image appearance features, implicit shape information and high-levelcontext together for further improving the volumetric segmentation performance.Extensive experiments on the challenging benchmark of brain segmentation frommagnetic resonance (MR) images corroborated the efficacy of our proposed methodin dealing with volumetric data. We believe this work unravels the potential of3D deep learning to advance the recognition performance on volumetric imagesegmentation.

VoxResNet: Deep Voxelwise Residual Networks for Volumetric Brain Segmentation