https://github.com/amedyukhina/cs-net

CS-Net (MICCAI 2019) and CS2-Net (MedIA 2020)

https://github.com/amedyukhina/cs-net

Repository

CS-Net (MICCAI 2019) and CS2-Net (MedIA 2020)

https://github.com/amedyukhina/CS-Net/blob/master/

# CS-Net: Channel and Spatial Attention Network for Curvilinear Structure Segmentation

Implementation of [CS-Net: Channel and Spatial Attention Network for Curvilinear Structure Segmentation](https://link.springer.com/chapter/10.1007/978-3-030-32239-7_80)

For the details of 3D extended version of CS-Net, please refer to [CS2-Net: Deep Learning Segmentation of Curvilinear Structures in Medical Imaging](cs2net.md)

---

## Overview


  

The main contribution of this work is the publication of two scarce datasets in the medical image field.  Plesae click the link below to access the details and source data. [![](https://img.shields.io/badge/Download-CORN--1-green)](http://www.imed-lab.com/?p=16073)

## Requirements

![](https://img.shields.io/badge/PyTorch-%3E%3D0.4.1-orange)  ![](https://img.shields.io/badge/tqdm-latest-orange)  ![](https://img.shields.io/badge/cv2-latest-orange)  ![](https://img.shields.io/badge/visdom-%3E%3D0.2.0-orange)  ![](https://img.shields.io/badge/sklearn-latest-orange)  

The attention module was implemented based on [DANet](https://github.com/junfu1115/DANet). The difference between the proposed module and the original block is that  we added a new 1x3 and 3x1 kernel convolution layer into spatial attention module. Plese refer to the paper for details.

## Get Started

Using the ```train.py``` and ```predict.py``` to train and test the model on your own dataset, respectively.

## Examples

- Vessel segmentation on Fundus


  


- Vessel segmentation on OCT-A images


  


- Nerve fiber tracing on CCM


  


## Citation

```
@inproceedings{mou2019cs,
title={CS-Net: channel and spatial attention network for curvilinear structure segmentation},
author={Mou, Lei and Zhao, Yitian and Chen, Li and Cheng, Jun and Gu, Zaiwang and Hao, Huaying and Qi, Hong and Zheng, Yalin and Frangi, Alejandro and Liu, Jiang},
booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
pages={721--730},
year={2019},
organization={Springer}
}
```



## Useful Links

| DRIVE          | http://www.isi.uu.nl/Research/Databases/DRIVE/              |
| :------------- | :---------------------------------------------------------- |
| **STARE**      | **http://www.ces.clemson.edu/ahoover/stare/**               |
| **IOSTAR**     | **http://www.retinacheck.org/**                             |
| **ToF MIDAS**  | **http://insight-journal.org/midas/community/view/21**      |
| **Synthetic**  | **https://github.com/giesekow/deepvesselnet/wiki/Datasets** |
| **VascuSynth** | **http://vascusynth.cs.sfu.ca/Data.html**                   |

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