Vessel Graph Network (VGN)

This is the pytorch implementation for "Deep Vessel Segmentation by Learning Graphical Connectivity".

Dependency

python3
pytorch 1.5
scikit-fmm 0.0.9
scikit-image 0.14.2
scikit-learn 0.22.2

Datasets

The VGN is evaluated on four retinal image datasets, namely the DRIVE, STARE, CHASE_DB1, and HRF datasets, which all are publicly available.
The coronary artery X-ray angiography (CA-XRA) dataset we additionally used for evaluation can not be shared regrettably.

Precomputed Results

We provide precomputed results of the VGN on the four retinal image datasets. [OneDrive]

Training a Model

We use a sequential training scheme composed of an initial pretraining of the CNN followed by joint training, including fine-tuning of the CNN module, of the whole VGN. Before the joint training, training graphs must be constructed from vessel probability maps inferred from the pretrained CNN.

CNN Pretraining

python train_CNN.py

Training Graph Construction

python Script.py

VGN Training

python train_VGN.py

Testing a Model

Run a test script among test_CNN.py, test_VGN.py

Demo Results

Two example results

CNN results

VGN results

Citation

@article{shin_media19,
  title = "Deep vessel segmentation by learning graphical connectivity",
  journal = "Medical Image Analysis",
  volume = "58",
  pages = "101556",
  year = "2019",
  issn = "1361-8415",
  doi = "https://doi.org/10.1016/j.media.2019.101556",
  url = "http://www.sciencedirect.com/science/article/pii/S1361841519300982",
  author = "Seung Yeon Shin and Soochahn Lee and Il Dong Yun and Kyoung Mu Lee",
}

w5688414 / pytorch-Vessel-Graph-Network