• EANet: Iterative Edge Attention Network for Medical Image Segmentation
• • • 1.1. 🔥NEWS🔥 :
  • Video Demo
  • Prerequisites
  • Clone repository
  • Download dataset
  • Training & Evaluation
  • Overview
  • • 1.2. Abstract
  • Overview framework
  • • 1.3. State-of-the-art Approaches
    • 1.4. Visualization Results
  • Citation

EANet: Iterative Edge Attention Network for Medical Image Segmentation

Authors:
Kun Wang,
Xiaohong Zhang,
Xiangbo Zhang,
Yuting Lu,
Sheng Huang,
Dan Yang

1.1. 🔥NEWS🔥 :

• [2021/04/22]🔥 Release the inference code!
• [2021/04/20] Create repository.

Video Demo

Prerequisites

• Python 3.5
• Pytorch 1.1
• OpenCV 4.0
• Numpy 1.15
• TensorboardX

Clone repository

git clone https://github.com/DLWK/EANet.git
cd EANet/

Download dataset

Download the datasets and unzip them into data folder

• COVID-19

Training & Evaluation

 cd train/
 python3 train.py

Overview

1.2. Abstract

Accurate and automatic segmentation of medical images can greatly assist the
clinical diagnosis and analysis. However, it remains a challenging task due to (1)
the diversity of scale in the medical image targets and (2) the complex context
environments of medical images, including ambiguity of structural boundaries,
complexity of shapes, and the heterogeneity of textures. To comprehensively
tackle these challenges, we propose a novel and effective iterative edge attention
network (EANet) for medical image segmentation with steps as follows. First,
we propose a dynamic scale-aware context (DSC) module, which dynamically
adjusts the receptive fields to extract multi-scale contextual information efficiently. Second, an edge-attention preservation (EAP) module is employed to
guide effectively removes noise and helps the edge stream focus on processing
only the boundary-related information. Finally, a multi-level pairwise regression
(MPR) module is designed to combine the complementary edge and region information for refining the ambiguous structure. This iterative optimization helps
to learn better representations and more accurate saliency maps. Extensive experimental results demonstrate that the proposed network achieves superior segmentation performance to state-of-the-art methods in four different challenging medical segmentation tasks, including lung nodule segmentation, COVID-19 infection segmentation, lung segmentation, and
thyroid nodule segmentation.

Overview framework

Figure 1: Overview of the proposed EANet. Our network is in an encoder-decoder style, it consists of the VGG-19 feature encoder E1 ∼ E5, dynamic scale-aware context (DSC) module, edge attention preservation (EAP) module, the feature decoder D1 ∼ D5 and multilevel pairwise regression (MPR) module. HFF denotes hybrid feature fusion block, which is a component of MPR. The final prediction is the generated global map (Sg) after iterative optimization by combining edge information and region information. The Si, {i = 2, 3, 4, 5} indicates the feature map of the side-output laye.

1.3. State-of-the-art Approaches

1. "CE-Net: Context encoder network for 2d medical image segmentation" TMI, 2019.
   doi:https://ieeexplore.ieee.org/abstract/document/8662594/
2. "Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net) for Medical Image Segmentation" 2018.
   doi: https://arxiv.org/abs/1802.06955
3. "Rethinking Atrous Convolution for Semantic Image Segmentation" IEEE TPAMI, 2018
   doi: https://arxiv.org/abs/1706.05587
4. "Unet++: A nested u-net architecture for medical image segmentation" IEEE TMI, 2019
   doi: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7329239/
5. "U-Net: Convolutional networks for biomed- ical image segmentation" MICCAI, 2015.
6. "FCN8s：Fully Convolutional Networks for Semantic Segmentation" CVPR, 2015.
   doi:https://arxiv.org/abs/1411.4038

1.4. Visualization Results

Figure 2:Visual comparison of COVID-19 infection segmentation results, where the red and green labels indicate the predicted segmentation and ground truth, respectively.

Figure 3:The visual comparison of results on the TN-SCUI dataset, where the blue and green labels indicate the predicted segmentation and ground truth, respectively

Figure 4:Visual comparison of feature maps for showing the effect of multi-level pairwise regression module (MPR). D5 ∼ D1 denote the feature maps of each decoder block. The odd and even rows show the baseline results without or with MPR, respectively.

Citation

• If you find this work is helpful, please cite our paper

@article{wang2022eanet,
  title={EANet: Iterative edge attention network for medical image segmentation},
  author={Wang, Kun and Zhang, Xiaohong and Zhang, Xiangbo and Lu, Yuting and Huang, Sheng and Yang, Dan},
  journal={Pattern Recognition},
  volume={127},
  pages={108636},
  year={2022},
  publisher={Elsevier}
}

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