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AtrialJSQnet
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# AtrialJSQnet ## Overview The repository contains the core codes of "[AtrialJSQnet: A New Framework for Joint Segmentation and Quantification of Left Atrium and Scars Incorporating Spatial and Shape Information](https://www.sciencedirect.com/science/article/pii/S1361841521003480)". The resposutory includes four folds: ### C++ script fold This fold includes some tools written using C++ for the pre-processing of LGE MRI. ### LearnGC fold This fold includes the python code to train and test the [LearnGC](https://www.sciencedirect.com/science/article/pii/S1361841519301355), which was published in MedIA2019. In this manuscript, we employed LearnGC for comparison. Note that the scripts to generate the multi-scale patches and the pre-processing code for LearnGC are not included here. For the complete version of this code, please kindly refer to https://github.com/Marie0909/LearnGC. ### Matlab fold This fold includes some pre-processing scripts employed in AtrialJSQnet, and some of these scripts aimed to use the generated C++ tools mentioned in the C++ script fold. ### Python script AtrialJSQnet This fold includes the python code for training and test the AtrialJSQnet. ## Dataset The dataset employed in this work is from [MICCAI 2018: Atrial Segmentation Challenge](http://www.cardiacatlas.org/challenges/left-atrium-fibrosis-and-scar-segmentation-challenge/). ## Releated work You may also be interested in following papers: 1. [Atrial scar quantification via multi-scale CNN in the graph-cuts framework](https://www.sciencedirect.com/science/article/pii/S1361841519301355) 2. [Medical Image Analysis on Left Atrial LGE MRI for Atrial Fibrillation Studies: A Review](https://arxiv.org/pdf/2106.09862.pdf) 3. [AtrialGeneral: Domain Generalization for Left Atrial Segmentation of Multi-center LGE MRIs](https://link.springer.com/chapter/10.1007/978-3-030-87231-1_54) ## Evaluation For evaluation, you could run LAScarQS2022_evaluate.py. Before runing LAScarQS2022_evaluate.py, you need to install SimpleITK, medpy and hausdorff by running "pip install SimpleITK/medpy/hausdorff". Also, note that this evaluation tool can only work in windows system as we only compiled the c++ tools in windows now, which are saved in the fold namely "tools". ## Cite If this code is useful for you, please kindly cite this work via: @article{journal/MedIA/li2022, title={Atrial{JSQ}net: a new framework for joint segmentation and quantification of left atrium and scars incorporating spatial and shape information}, author={Li, Lei and Zimmer, Veronika A and Schnabel, Julia A and Zhuang, Xiahai}, journal={Medical Image Analysis}, volume={76}, pages={102303}, year={2022}, publisher={Elsevier} } If you have any questions, you are always welcome to contact with lilei.sky@outlook.com.
