# VoxResNet: Deep Voxelwise Residual Networks for Volumetric Brain Segmentation

arXiv: https://arxiv.org/abs/1608.05895 · HTML: https://mlsanity.com/paper/1608.05895/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Aug 2016.

## Built on by (most cited first, 10 of 10)

- [Medical Image Segmentation with 3D Convolutional Neural Networks: A Survey](https://mlsanity.com/paper/2108.08467/) (2021)
- [Optimized CNNs for Rapid 3D Point Cloud Object Recognition](https://mlsanity.com/paper/2412.02855/) (2024)
- [Voxel-level Siamese Representation Learning for Abdominal Multi-Organ Segmentation](https://mlsanity.com/paper/2105.07672/) (2021)
- [Cross-Modality Brain Tumor Segmentation via Bidirectional Global-to-Local Unsupervised Domain Adaptation](https://mlsanity.com/paper/2105.07715/) (2021)
- [Comparative Evaluation of 3D and 2D Deep Learning Techniques for Semantic Segmentation in CT Scans](https://mlsanity.com/paper/2101.07612/) (2021)
- [FLNeRF: 3D Facial Landmarks Estimation in Neural Radiance Fields](https://mlsanity.com/paper/2211.11202/) (2022)
- [Cardiac Segmentation on CT Images through Shape-Aware Contour Attentions](https://mlsanity.com/paper/2105.13153/) (2021)
- [On Sensitivity and Robustness of Normalization Schemes to Input Distribution Shifts in Automatic MR Image Diagnosis](https://mlsanity.com/paper/2306.13276/) (2023)
- [Effect of Input Size on the Classification of Lung Nodules Using Convolutional Neural Networks](https://mlsanity.com/paper/2107.05085/) (2021)
- [Scalable 3D Semantic Segmentation for Gun Detection in CT Scans](https://mlsanity.com/paper/2112.03917/) (2021)

## Abstract (arXiv)

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.