Inter-Slice Context Residual Learning for 3D Medical Image Segmentation

Published in

IEEE Transactions on Medical Imaging(2020)

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TL;DR

The results indicate that the proposed ConResNet is more accurate than six top-ranking methods in brain tumor segmentation and seven top- ranking methods in pancreas segmentation.

Abstract

Automated and accurate 3D medical image segmentation plays an essential role in assisting medical professionals to evaluate disease progresses and make fast therapeutic schedules. Although deep convolutional neural networks (DCNNs) have widely applied to this task, the accuracy of these models still need to be further improved mainly due to their limited ability to 3D context perception. In this paper, we propose the 3D context residual network (ConResNet) for the accurate segmentation of 3D medical images. This model consists of an encoder, a segmentation decoder, and a context residual decoder. We design the context residual module and use it to bridge both decoders at each scale. Each context residual module contains both context residual mapping and context attention mapping, the formal aims to explicitly learn the inter-slice context information and the latter uses such context as a kind of attention to boost the segmentation accuracy. We evaluated this model on the MICCAI 2018 Brain Tumor Segmentation (BraTS) dataset and NIH Pancreas Segmentation (Pancreas-CT) dataset. Our results not only demonstrate the effectiveness of the proposed 3D context residual learning scheme but also indicate that the proposed ConResNet is more accurate than six top-ranking methods in brain tumor segmentation and seven top-ranking methods in pancreas segmentation.

Authors

Jianpeng Zhang

3 papers

Yutong Xie

4 papers

Yong Xia

2 papers

References55 items

SESV: Accurate Medical Image Segmentation by Predicting and Correcting Errors

Deep Reinforcement Learning for Weakly-Supervised Lymph Node Segmentation in CT Images

3D APA-Net: 3D Adversarial Pyramid Anisotropic Convolutional Network for Prostate Segmentation in MR Images

Models Genesis: Generic Autodidactic Models for 3D Medical Image Analysis

Light-Weight Hybrid Convolutional Network for Liver Tumor Segmentation

Inter-Slice Context Residual Learning for 3D Medical Image Segmentation

Published in

IEEE Transactions on Medical Imaging(2020)

External Links:

Generate Graph DownloadPDF

TL;DR

The results indicate that the proposed ConResNet is more accurate than six top-ranking methods in brain tumor segmentation and seven top- ranking methods in pancreas segmentation.

Abstract

Authors

Jianpeng Zhang

3 papers

Yutong Xie

4 papers

Yong Xia

2 papers

References55 items

SESV: Accurate Medical Image Segmentation by Predicting and Correcting Errors

Deep Reinforcement Learning for Weakly-Supervised Lymph Node Segmentation in CT Images

3D APA-Net: 3D Adversarial Pyramid Anisotropic Convolutional Network for Prostate Segmentation in MR Images

Models Genesis: Generic Autodidactic Models for 3D Medical Image Analysis

Light-Weight Hybrid Convolutional Network for Liver Tumor Segmentation

Multi-Scale Self-Guided Attention for Medical Image Segmentation

One-Pass Multi-Task Networks With Cross-Task Guided Attention for Brain Tumor Segmentation

Fully Automated Pancreas Segmentation with Two-stage 3D Convolutional Neural Networks

Learning Active Contour Models for Medical Image Segmentation

Reducing the Hausdorff Distance in Medical Image Segmentation With Convolutional Neural Networks

3D Dilated Multi-Fiber Network for Real-time Brain Tumor Segmentation in MRI

DCSR: Dilated Convolutions for Single Image Super-Resolution

Med3D: Transfer Learning for 3D Medical Image Analysis

Weight Standardization

A Mutual Bootstrapping Model for Automated Skin Lesion Segmentation and Classification

Deeply supervised 3D fully convolutional networks with group dilated convolution for automatic MRI prostate segmentation

Attention Residual Learning for Skin Lesion Classification

Boundary loss for highly unbalanced segmentation

3D MRI brain tumor segmentation using autoencoder regularization

Learning Contextual and Attentive Information for Brain Tumor Segmentation

A Novel Bayesian Model Incorporating Deep Neural Network and Statistical Shape Model for Pancreas Segmentation

Ensembles of Densely-Connected CNNs with Label-Uncertainty for Brain Tumor Segmentation

Automatic Brain Tumor Segmentation using Convolutional Neural Networks with Test-Time Augmentation

Pyramid Dilated Deeper ConvLSTM for Video Salient Object Detection

Attention Gated Networks: Learning to Leverage Salient Regions in Medical Images

Revisiting Dilated Convolution: A Simple Approach for Weakly- and Semi-Supervised Semantic Segmentation

Abdominal multi-organ segmentation with organ-attention networks and statistical fusion

Training Multi-organ Segmentation Networks with Sample Selection by Relaxed Upper Confident Bound

3D multi‐scale FCN with random modality voxel dropout learning for Intervertebral Disc Localization and Segmentation from Multi‐modality MR Images

Pancreas Segmentation in CT and MRI Images via Domain Specific Network Designing and Recurrent Neural Contextual Learning

Group Normalization

Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation

Joint Optic Disc and Cup Segmentation Based on Multi-Label Deep Network and Polar Transformation

A 3D Coarse-to-Fine Framework for Automatic Pancreas Segmentation

3D deeply supervised network for automated segmentation of volumetric medical images

Recurrent Saliency Transformation Network: Incorporating Multi-stage Visual Cues for Small Organ Segmentation

Squeeze-and-Excitation Networks

Liver lesion segmentation informed by joint liver segmentation

Residual Attention Network for Image Classification

Automated Melanoma Recognition in Dermoscopy Images via Very Deep Residual Networks

Large Kernel Matters — Improve Semantic Segmentation by Global Convolutional Network

Automatic tumor segmentation using convolutional neural networks

A survey on deep learning in medical image analysis

Spatial aggregation of holistically‐nested convolutional neural networks for automated pancreas localization and segmentation☆

Feature Pyramid Networks for Object Detection

Pyramid Scene Parsing Network

3D Deeply Supervised Network for Automatic Liver Segmentation from CT Volumes

DCAN: Deep Contour-Aware Networks for Accurate Gland Segmentation

Deep Residual Learning for Image Recognition

Multi-Scale Context Aggregation by Dilated Convolutions

The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)

DeepOrgan: Multi-level Deep Convolutional Networks for Automated Pancreas Segmentation

U-Net: Convolutional Networks for Biomedical Image Segmentation

Multi-scale guided attention for medical image segmentation

Field of Study

Computer ScienceEngineeringMedicine

Journal Information

Name

Ieee Transactions on Medical Imaging

Page

1285 - 1298

Volume

Venue Information

Name

IEEE Transactions on Medical Imaging

Type

journal

URL

http://ieeexplore.ieee.org/servlet/opac?punumber=42

Alternate Names

IEEE Trans Med Imaging

TL;DR

Abstract

Authors

References55 items

SESV: Accurate Medical Image Segmentation by Predicting and Correcting Errors

Deep Reinforcement Learning for Weakly-Supervised Lymph Node Segmentation in CT Images

3D APA-Net: 3D Adversarial Pyramid Anisotropic Convolutional Network for Prostate Segmentation in MR Images

Models Genesis: Generic Autodidactic Models for 3D Medical Image Analysis

Light-Weight Hybrid Convolutional Network for Liver Tumor Segmentation

TL;DR

Abstract

Authors

References55 items

SESV: Accurate Medical Image Segmentation by Predicting and Correcting Errors

Deep Reinforcement Learning for Weakly-Supervised Lymph Node Segmentation in CT Images

3D APA-Net: 3D Adversarial Pyramid Anisotropic Convolutional Network for Prostate Segmentation in MR Images

Models Genesis: Generic Autodidactic Models for 3D Medical Image Analysis

Light-Weight Hybrid Convolutional Network for Liver Tumor Segmentation

Multi-Scale Self-Guided Attention for Medical Image Segmentation

One-Pass Multi-Task Networks With Cross-Task Guided Attention for Brain Tumor Segmentation

Fully Automated Pancreas Segmentation with Two-stage 3D Convolutional Neural Networks

Learning Active Contour Models for Medical Image Segmentation

Reducing the Hausdorff Distance in Medical Image Segmentation With Convolutional Neural Networks

3D Dilated Multi-Fiber Network for Real-time Brain Tumor Segmentation in MRI

DCSR: Dilated Convolutions for Single Image Super-Resolution

Med3D: Transfer Learning for 3D Medical Image Analysis

Weight Standardization

A Mutual Bootstrapping Model for Automated Skin Lesion Segmentation and Classification

Deeply supervised 3D fully convolutional networks with group dilated convolution for automatic MRI prostate segmentation

Attention Residual Learning for Skin Lesion Classification

Boundary loss for highly unbalanced segmentation

No New-Net

3D MRI brain tumor segmentation using autoencoder regularization

Learning Contextual and Attentive Information for Brain Tumor Segmentation

A Novel Bayesian Model Incorporating Deep Neural Network and Statistical Shape Model for Pancreas Segmentation

Ensembles of Densely-Connected CNNs with Label-Uncertainty for Brain Tumor Segmentation

Automatic Brain Tumor Segmentation using Convolutional Neural Networks with Test-Time Augmentation

Pyramid Dilated Deeper ConvLSTM for Video Salient Object Detection

Attention Gated Networks: Learning to Leverage Salient Regions in Medical Images

Revisiting Dilated Convolution: A Simple Approach for Weakly- and Semi-Supervised Semantic Segmentation

Abdominal multi-organ segmentation with organ-attention networks and statistical fusion

Training Multi-organ Segmentation Networks with Sample Selection by Relaxed Upper Confident Bound

3D multi‐scale FCN with random modality voxel dropout learning for Intervertebral Disc Localization and Segmentation from Multi‐modality MR Images

Pancreas Segmentation in CT and MRI Images via Domain Specific Network Designing and Recurrent Neural Contextual Learning

Group Normalization

Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation

Joint Optic Disc and Cup Segmentation Based on Multi-Label Deep Network and Polar Transformation

A 3D Coarse-to-Fine Framework for Automatic Pancreas Segmentation

3D deeply supervised network for automated segmentation of volumetric medical images

Recurrent Saliency Transformation Network: Incorporating Multi-stage Visual Cues for Small Organ Segmentation

Squeeze-and-Excitation Networks

Liver lesion segmentation informed by joint liver segmentation

Residual Attention Network for Image Classification

Automated Melanoma Recognition in Dermoscopy Images via Very Deep Residual Networks

Large Kernel Matters — Improve Semantic Segmentation by Global Convolutional Network

Automatic tumor segmentation using convolutional neural networks

A survey on deep learning in medical image analysis

Spatial aggregation of holistically‐nested convolutional neural networks for automated pancreas localization and segmentation☆

Feature Pyramid Networks for Object Detection

Pyramid Scene Parsing Network

3D Deeply Supervised Network for Automatic Liver Segmentation from CT Volumes

DCAN: Deep Contour-Aware Networks for Accurate Gland Segmentation

Deep Residual Learning for Image Recognition

Multi-Scale Context Aggregation by Dilated Convolutions

The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)

DeepOrgan: Multi-level Deep Convolutional Networks for Automated Pancreas Segmentation

U-Net: Convolutional Networks for Biomedical Image Segmentation

Multi-scale guided attention for medical image segmentation

Field of Study

Journal Information

Name

Page

Volume

Venue Information

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Type

URL

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