VoxResNet: Deep Voxelwise Residual Networks for Volumetric Brain Segmentation

TL;DR

This paper introduces VoxResNet, a deep 3D residual network for volumetric brain segmentation, which effectively leverages contextual information and improves segmentation accuracy on MRI data.

Contribution

The paper presents a novel 3D deep residual network architecture, VoxResNet, and an auto-context extension that enhances volumetric brain segmentation performance.

Findings

VoxResNet outperforms existing methods on brain MRI segmentation benchmarks.

Auto-context integration further improves segmentation accuracy.

Deep 3D residual learning effectively captures volumetric contextual information.

Abstract

Recently deep residual learning with residual units for training very deep neural networks advanced the state-of-the-art performance on 2D image recognition tasks, e.g., object detection and segmentation. However, how to fully leverage contextual representations for recognition tasks from volumetric data has not been well studied, especially in the field of medical image computing, where a majority of image modalities are in volumetric format. In this paper we explore the deep residual learning on the task of volumetric brain 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 recognition tasks, and is extended into a 3D variant for handling volumetric data. Second, an auto-context version of VoxResNet is proposed by seamlessly integrating the low-level image appearance features, implicit shape information and high-level context together for further improving the volumetric segmentation performance. Extensive experiments on the challenging benchmark of brain segmentation from magnetic resonance (MR) images corroborated the efficacy of our proposed method in dealing with volumetric data. We believe this work unravels the potential of 3D deep learning to advance the recognition performance on volumetric image segmentation.