{"references": ["Zhang M, Wu Y, Zhang H, et al. Multi-site, Multi-domain Airway Tree Modeling (ATM'22): A Public Benchmark for Pulmonary Airway Segmentation[J]. arXiv preprint arXiv:2303.05745, 2023.", "Zhang M, Zhang H, Yang G Z, et al. CFDA: Collaborative Feature Disentanglement and Augmentation for Pulmonary Airway Tree Modeling of COVID-19 CTs[C]//Medical Image Computing and Computer Assisted Intervention\u2013MICCAI 2022: 25th International Conference, Singapore, September 18\u201322, 2022, Proceedings, Part I. Cham: Springer Nature Switzerland, 2022: 506-516.", "Zheng H, Qin Y, Gu Y, et al. Alleviating class-wise gradient imbalance for pulmonary airway segmentation[J]. IEEE Transactions on Medical Imaging, 2021, 40(9): 2452-2462.", "Yu W, Zheng H, Zhang M, et al. BREAK: Bronchi Reconstruction by gEodesic transformation And sKeleton embedding[C]//2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI). IEEE, 2022: 1-5.", "Qin Y, Chen M, Zheng H, et al. Airwaynet: a voxel-connectivity aware approach for accurate airway segmentation using convolutional neural networks[C]//International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, Cham, 2019: 212-220."]}

Dataset for the MICCAI-2022-Challenge: Airway Tree Modeling (ATM'22) This is the TrainBatch2. For the training set of EXACT09 [1], the participants should download the images from by themselves. We only provide the full airway annotation of the training set of EXACT09. We provide the casename correspondence information in the TrainBatch2_CaseInfo.csv file. It is convinient for researchers to locate the original cases in LIDC-IDRI with our provided full airway annotation for other research purposes. [1] Lo P, Van Ginneken B, Reinhardt J M, et al. Extraction of airways from CT (EXACT'09)[J]. IEEE Transactions on Medical Imaging, 2012, 31(11): 2093-2107. If you use this dataset in your research, you must cite the papers in the References below !!!