The repository contains the core codes of "Atrial scar quantification via multi-scale CNN in the graph-cuts framework". The resposutory includes three folds:

This fold includes the original C++ scripts to generate the multi-scale patches and the pre-processing code for LearnGC.

This fold includes the python code to train and test the LearnGC architecture.

This fold includes some pre-processing scripts employed in LearnGC, and some of these scripts aimed to use the generated C++ tools mentioned in the C++ script fold.

Besides, GenerateModelsFromLabels.cxx was used to convert 3D label into 3D mesh.

You may also be interested in following papers:

1. AtrialJSQnet: A New Framework for Joint Segmentation and Quantification of Left Atrium and Scars Incorporating Spatial and Shape Information
2. Medical Image Analysis on Left Atrial LGE MRI for Atrial Fibrillation Studies: A Review
3. AtrialGeneral: Domain Generalization for Left Atrial Segmentation of Multi-center LGE MRIs

If this code is useful for you, please kindly cite this work via:

@article{journal/MedIA/li2020,
title={Atrial scar quantification via multi-scale {CNN} in the graph-cuts framework},
author={Li, Lei and Wu, Fuping and Yang, Guang and Xu, Lingchao and Wong, Tom and Mohiaddin, Raad and Firmin, David and Keegan, Jennifer and Zhuang, Xiahai},
journal={Medical image analysis},
volume={60},
pages={101595},
year={2020},
publisher={Elsevier}
}

If you have any questions, you are always welcome to contact with [email protected].