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CS2-Net: Deep Learning Segmentation of Curvilinear Structures in Medical Imaging

Implementation of CS2-Net MedIA 2020

Overview

Requirements

Get Started

  • train3d.py is used to train the 3D segmentation network.

  • predict3d.py is used to test the trained model.

  • Please note that you should change the dataloader definition in train3d.py.

Examples

  • MRA brain vessel segmentation

  • Synthetic & VascuSynth

Citation

@article{mou2020cs2,
title={CS2-Net: Deep Learning Segmentation of Curvilinear Structures in Medical Imaging},
author={Mou, Lei and Zhao, Yitian and Fu, Huazhu and Liux, Yonghuai and Cheng, Jun and Zheng, Yalin and Su, Pan and Yang, Jianlong and Chen, Li and Frangi, Alejandro F and others},
journal={Medical Image Analysis},
pages={101874},
year={2020},
publisher={Elsevier}
}

Corrections to: CS2-Net- Deep learning segmentation of curvilinear structures in medical imaging

The original comparison results in Table 8 on page 14 are:

The corrected comparison results are:

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