lemoshu / inf-net

code for "Inf-Net: Automatic COVID-19 Lung Infection Segmentation from CT Scans" submit to TMI2020.

Inf-Net: Automatic COVID-19 Lung Infection Segmentation from CT Scans

Authors: Deng-Ping Fan, Tao Zhou, Ge-Peng Ji, Yi Zhou, Geng Chen, Huazhu Fu, Jianbing Shen, and Ling Shao.

• This repository provides code for "Inf-Net: Automatic COVID-19 Lung Infection Segmentation from CT Scans" submit to TMI-2020. (arXiv Pre-print & medrXiv)

• Any quesetions please contact to Deng-Ping Fan via E-mail.

1. Introduction

1.1. Task Description

Figure 1. Example of COVID-19 infected regions in CT axial slice, where the red and green masks denote the ground-glass opacity (GGO) and consolidation, respectively. The images are collected from [1].

[1] COVID-19 CT segmentation dataset, link: https://medicalsegmentation.com/covid19/, accessed: 2020-04-11.

2. Proposed Methods

Our proposed methods consist of three individual components under different settings:

• Inf-Net (Supervised Learning with segmentation and edge supervision).

• Semi-Inf-Net (Semi-supervised learning with doctor label and pseudo label)

• Semi-Inf-Net + FCN8s/UNet (Extension to Multi-class Segmentation, including Background, Ground-glass opacities, and Consolidation).

2.1. Inf-Net

2.1.1 Overview

Figure 2. The architecture of our proposed Inf-Net model, which consists of three reverse attention (RA) modules connected to the paralleled partial decoder (PPD).

2.1.2. Usage

• Pre-trained Model

  Coming soon ...

2.2. Semi-Inf-Net

2.2.1. Overview

Figure 3. Overview of the proposed Semi-supervised Inf-Net framework.

2.2.2. Usage

• Pre-trained Model

  Coming soon ...

2.3. Semi-Inf-Net + FCN8s

2.3.1. Overview

2.3.2. Usage

• Pre-trained Model

  Coming soon ...

3. Evaluation Toolbox

3.1. Introduction

We provide one-key evaluation toolbox for LungInfection Segmentation tasks, including Lung-Infection and Multi-Class-Infection.

Google Drive: https://drive.google.com/open?id=1BGUUmrRPOWPxdxnawFnG9TVZd8rwLqCF

3.2. Usage

• Prerequisites: MATLAB Software (Windows/Linux OS is both work, however we suggest you test it in the Linux OS for convenience.)

• run cd ./Evaluation/ and matlab open the matlab software via terminal

• Just run main.m to get the overall evaluation results.

• Edit the parameters in the main.m to evaluate your custom methods. Please refer to the instructions in the main.m

4. COVID-SemiSeg Dataset

We also build a semi-supervised COVID-19 infection segmentation (COVID-SemiSeg) dataset, with 100 labeled CT scans from the COVID-19 CT Segmentation dataset [1] and 1600 unlabeled images from the COVID-19 CT Collection dataset [2]. Our COVID-SemiSeg Dataset can be downloaded at Google Drive

[1]“COVID-19 CT segmentation dataset,” https://medicalsegmentation.com/covid19/, accessed: 2020-04-11. [2]J. P. Cohen, P. Morrison, and L. Dao, “COVID-19 image data collection,” arXiv, 2020.

3.1. Training set

1. Lung infection which consists of 50 labels by doctors (Doctor-label) and 1600 pesudo labels generated (Pesudo-label) by our Semi-Inf-Net model.
   Download: http://dpfan.net/wp-content/uploads/LungInfection-Train.zip

2. Multi-Class lung infection which also composed of 50 multi-class labels (GT) by doctors and 50 lung infection labels (Prior) generated by our Semi-Inf-Net model.
   Download: http://dpfan.net/wp-content/uploads/MultiClassInfection-Train.zip

3.2. Testing set

1. The Lung infection segmentation set contains 48 images associate with 48 GT. Download: http://dpfan.net/wp-content/uploads/LungInfection-Test.zip

2. The Multi-Class lung infection segmentation set has 48 images and 48 GT. Download: http://dpfan.net/wp-content/uploads/MultiClassInfection-Test.zip

4. Results

To compare the infection regions segmentation performance, we consider the two state-of-the-art models U-Net and U-Net++. We also show the multi-class infection labeling results in Fig. 5. As can be observed, our model, Semi-Inf-Net & FCN8s, consistently performs the best among all methods. It is worth noting that both GGO and consolidation infections are accurately segmented by Semi-Inf-Net & FCN8s, which further demonstrates the advantage of our model. In contrast, the baseline methods, DeepLabV3+ with different strides and FCNs, all obtain unsatisfactory results, where neither GGO and consolidation infections can be accurately segmented.

Overall results: http://dpfan.net/wp-content/uploads/COVID-SemiSeg-Results.zip

4.1. Download link:

Lung infection segmentation results: http://dpfan.net/wp-content/uploads/Lung-infection-segmentation.zip
Multi-class lung infection segmentation: http://dpfan.net/wp-content/uploads/Multi-class-lung-infection-segmentation.zip

5. Visualization Results:

Figure 4. Visual comparison of lung infection segmentation results.

Figure 5. Visual comparison of multi-class lung infection segmentation results, where the red and green labels indicate the GGO and consolidation, respectively.

6. Paper list of COVID-19 related (update continue)

https://github.com/HzFu/COVID19_imaging_AI_paper_list

7. Manuscript

http://dpfan.net/wp-content/uploads/2020TMISubmissionInfNet.pdf

8. Citation

Please cite our paper if you find the work useful:

@article{fan2020InfNet,
title={Inf-Net: Automatic COVID-19 Lung Infection Segmentation from CT Scans},
author={Fan, Deng-Ping and Zhou, Tao and Ji, Ge-Peng and Zhou, Yi and Chen, Geng and Fu, Huazhu and Shen, Jianbing and Shao, Ling},
Journal = {arXiv},
year={2020}
}

9. Acknowledgements

We thanks xxx for their contributions.