Awesome

Transformer Network for Significant Stenosis Detection in CCTA of Coronary Arteries

The implementation of our MICCAI2021 paper "Transformer Network for Significant Stenosis Detection in CCTA of Coronary Arteries".

Requirements

Python 3.6, PyTorch 1.6 and other common packages are listed in requirements.txt

Usage

Volume sequences can be obtained from MPR images through data_maker.py.

Cubic volumes are flattened and combined with the corresponding labels to consist of a 1D vectors, and image information sequences are obtained by concatenating these vectors.

Both training data and test data are saved as numpy arrays of shape (D, L, N^3), where D indicates the number of data on centerline-level.

The path of training data and test data can be set in config.py, for example:

train_dataset_root = './dataset/train_dataset.npy'
test_dataset_root = './dataset/test_dataset.npy'

Citation

Please consider citing the project in your publications if it helps your research. The following is a BibTeX reference. The BibTeX entry requires the url LaTeX package.

@article{ma2021transformer,
  title={Transformer Network for Significant Stenosis Detection in CCTA of Coronary Arteries},
  author={Ma, Xinghua and Luo, Gongning and Wang, Wei and Wang, Kuanquan},
  journal={arXiv preprint arXiv:2107.03035},
  year={2021}
}