VoxResNet: Deep Voxelwise Residual Networks for Volumetric Brain Segmentation

Published in

arXiv.org(2016)

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TL;DR

This work unravels the potential of 3D deep learning to advance the recognition performance on volumetric image segmentation and proposes 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 handlingvolumetric data.

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.

Authors

Q. Dou

14 papers

P. Heng

25 papers

Hao Chen

9 papers

References41 items

Automatic brain tissue segmentation in MR images using Random Forests and Conditional Random Fields

3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation

Weighted residuals for very deep networks

Wide Residual Networks

Fully convolutional networks for multi-modality isointense infant brain image segmentation

VoxResNet: Deep Voxelwise Residual Networks for Volumetric Brain Segmentation

Published in

arXiv.org(2016)

External Links:

Generate Graph

TL;DR

Abstract

Authors

Q. Dou

14 papers

P. Heng

25 papers

Hao Chen

9 papers

References41 items

Automatic brain tissue segmentation in MR images using Random Forests and Conditional Random Fields

3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation

Weighted residuals for very deep networks

Wide Residual Networks

Fully convolutional networks for multi-modality isointense infant brain image segmentation

Efficient multi‐scale 3D CNN with fully connected CRF for accurate brain lesion segmentation

Identity Mappings in Deep Residual Networks

Deep Contextual Networks for Neuronal Structure Segmentation

Automatic Detection of Cerebral Microbleeds From MR Images via 3D Convolutional Neural Networks

Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning

Instance-Aware Semantic Segmentation via Multi-task Network Cascades

Deep Residual Learning for Image Recognition

MRBrainS Challenge: Online Evaluation Framework for Brain Image Segmentation in 3T MRI Scans

Deep End2End Voxel2Voxel Prediction

The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)

Parallel Multi-Dimensional LSTM, With Application to Fast Biomedical Volumetric Image Segmentation

U-Net: Convolutional Networks for Biomedical Image Segmentation

Standard Plane Localization in Fetal Ultrasound via Domain Transferred Deep Neural Networks

Automatic detection of cerebral microbleeds via deep learning based 3D feature representation

Evaluation of an automatic brain segmentation method developed for neonates on adult MR brain images

Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift

Fully convolutional networks for semantic segmentation

LINKS: Learning-based multi-source IntegratioN frameworK for Segmentation of infant brain images

Deeply-Supervised Nets

Going deeper with convolutions

Deep Learning Based Imaging Data Completion for Improved Brain Disease Diagnosis

Very Deep Convolutional Networks for Large-Scale Image Recognition

Caffe: Convolutional Architecture for Fast Feature Embedding

A New 2.5D Representation for Lymph Node Detection Using Random Sets of Deep Convolutional Neural Network Observations

Multi-Atlas Brain MRI Segmentation with Multiway Cut

Automated Brain-Tissue Segmentation by Multi-Feature SVM Classification

Deep Feature Learning for Knee Cartilage Segmentation Using a Triplanar Convolutional Neural Network

Clinical use of brain volumetry

ImageNet classification with deep convolutional neural networks

Auto-Context and Its Application to High-Level Vision Tasks and 3D Brain Image Segmentation

Multi-atlas based segmentation of brain images: Atlas selection and its effect on accuracy

Combination Strategies in Multi-Atlas Image Segmentation: Application to Brain MR Data

Auto-context and its application to high-level vision tasks

ISLES 2015 ‐ A public evaluation benchmark for ischemic stroke lesion segmentation from multispectral MRI

Field of Study

Computer Science

Journal Information

Name

ArXiv

Volume

abs/2005.00687

Venue Information

Name

arXiv.org

Type

URL

https://arxiv.org

Alternate Names

ArXiv

TL;DR

Abstract

Authors

References41 items

Automatic brain tissue segmentation in MR images using Random Forests and Conditional Random Fields

3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation

Weighted residuals for very deep networks

Wide Residual Networks

Fully convolutional networks for multi-modality isointense infant brain image segmentation

TL;DR

Abstract

Authors

References41 items

Automatic brain tissue segmentation in MR images using Random Forests and Conditional Random Fields

3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation

Weighted residuals for very deep networks

Wide Residual Networks

Fully convolutional networks for multi-modality isointense infant brain image segmentation

Efficient multi‐scale 3D CNN with fully connected CRF for accurate brain lesion segmentation

Identity Mappings in Deep Residual Networks

Deep Contextual Networks for Neuronal Structure Segmentation

Automatic Detection of Cerebral Microbleeds From MR Images via 3D Convolutional Neural Networks

Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning

Instance-Aware Semantic Segmentation via Multi-task Network Cascades

Deep Residual Learning for Image Recognition

MRBrainS Challenge: Online Evaluation Framework for Brain Image Segmentation in 3T MRI Scans

Deep End2End Voxel2Voxel Prediction

The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)

Parallel Multi-Dimensional LSTM, With Application to Fast Biomedical Volumetric Image Segmentation

U-Net: Convolutional Networks for Biomedical Image Segmentation

Standard Plane Localization in Fetal Ultrasound via Domain Transferred Deep Neural Networks

Automatic detection of cerebral microbleeds via deep learning based 3D feature representation

Evaluation of an automatic brain segmentation method developed for neonates on adult MR brain images

Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift

Fully convolutional networks for semantic segmentation

LINKS: Learning-based multi-source IntegratioN frameworK for Segmentation of infant brain images

Deeply-Supervised Nets

Going deeper with convolutions

Deep Learning Based Imaging Data Completion for Improved Brain Disease Diagnosis

Very Deep Convolutional Networks for Large-Scale Image Recognition

Caffe: Convolutional Architecture for Fast Feature Embedding

A New 2.5D Representation for Lymph Node Detection Using Random Sets of Deep Convolutional Neural Network Observations

Multi-Atlas Brain MRI Segmentation with Multiway Cut

Automated Brain-Tissue Segmentation by Multi-Feature SVM Classification

Deep Feature Learning for Knee Cartilage Segmentation Using a Triplanar Convolutional Neural Network

Clinical use of brain volumetry

ImageNet classification with deep convolutional neural networks

Auto-Context and Its Application to High-Level Vision Tasks and 3D Brain Image Segmentation

Multi-atlas based segmentation of brain images: Atlas selection and its effect on accuracy

Combination Strategies in Multi-Atlas Image Segmentation: Application to Brain MR Data

Et al

Auto-context and its application to high-level vision tasks

ISLES 2015 ‐ A public evaluation benchmark for ischemic stroke lesion segmentation from multispectral MRI

and R

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Volume

Venue Information

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Type

URL

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