A total of 154 3D MRIs from patients with AF are used in for the purpose of this challenge. The original resolution of the data is 0.625 x 0.625 x 0.625 mm³. A large proportion of data were kindly provided by The University of Utah (NIH/NIGMS Center for Integrative Biomedical Computing (CIBC)), while the rest were from multiple other institutes. All clinical data have obtained institutional ethics approval. Each 3D MRI patient data was acquired using a clinical whole-body MRI scanner and contained raw the MRI scan and the corresponding ground truth labels for the left atrial (LA) cavity. The ground truths were manually segmented by experts in the field. The raw MRIs are in grayscale and the segmentation labels are in binary (255 = positive, 0 = negative). The dimensions of the MRIs may vary depending on each patient, however, all MRIs contain exactly 88 slices in the Z axis.

The dataset is split such that 100 patient data are used for training, and 54 patient data will be used for testing and evaluation. The participants will have access to all the MRIs and their respective labels (LA cavity mask) in the training set, and only the MRIs in the testing set.

To deter manual segmentation, the test data will be released 2 weeks prior to the end of the challenge such that participants should first develop replica tag heuer their model on the training set, and then submit predictions for the test set within the 2-week window.

The supporting files contain the labels for whether a patient pre or post ablation, as well as MRA sequences if they are available for the particular patient.

Competition start
  • All 100 training data for LA  cavity segmentation released (data + mask)
2 weeks before competition deadline
  • 54 test data for LA cavity segmentation released (data only)

The files are arranged such that each individual file contains one patient data. For each data, the raw MRI “lgemri.nrrd”, the LA cavity segmentation “laendo.nrrd” are provided. “.nrrd” is a medical imaging file format, and can be read using various programming languages.

File descriptions

You will have access to the following:

  • Training Set (folder) – the entire training set (100 MRIs and LA cavity labels)
  • Test Set (folder) – the entire testing set (54 MRIs and LA cavity labels)

Please Cite this Data Source in Any Future Publication:
Zhaohan Xiong, Qing Xia, Zhiqiang Hu, Ning Huang, Cheng Bian, Yefeng Zheng, Sulaiman Vesal, Nishant Ravikumar, Andreas Maier, Xin Yang, Pheng-Ann Heng, Dong Ni, Caizi Li, Qianqian Tong, Weixin Si, Elodie Puybareau, Younes Khoudli, Thierry Géraud, Chen Chen, Wenjia Bai, Daniel Rueckert, Lingchao Xu, Xiahai Zhuang, Xinzhe Luo, Shuman Jia, Maxime Sermesant, Yashu Liu, Kuanquan Wang, Davide Borra, Alessandro Masci, Cristiana Corsi, Coen de Vente, Mitko Veta, Rashed Karim, Chandrakanth Jayachandran Preetha, Sandy Engelhardt, Menyun Qiao, Yuanyuan Wang, Qian Tao, Marta Nuñez-Garcia, Oscar Camara, Nicolo Savioli, Pablo Lamata, Jichao Zhao. “A Global Benchmark of Algorithms for Segmenting the Left Atrium from Late Gadolinium-Enhanced Cardiac Magnetic Resonance Imaging.” Medical Image Analysis 67 (2021): 101832.

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