@article{
 title = {Multi-site, Multi-domain Airway Tree Modeling (ATM'22): A Public Benchmark for Pulmonary Airway Segmentation},
 type = {article},
 year = {2023},
 keywords = {methods,pulmonary airway segmen-,tation,topological prior knowledge,traditional and deep-learning},
 websites = {http://arxiv.org/abs/2303.05745},
 id = {7ede210d-4e55-3f76-a81d-eb2dd5b05186},
 created = {2024-01-13T07:02:56.915Z},
 file_attached = {true},
 profile_id = {d5b9f595-4104-33d2-b640-e84dee1b7ded},
 last_modified = {2024-01-13T08:14:19.400Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {Open international challenges are becoming the de facto standard for assessing computer vision and image analysis algorithms. In recent years, new methods have extended the reach of pulmonary airway segmentation that is closer to the limit of image resolution. Since EXACT'09 pulmonary airway segmentation, limited effort has been directed to quantitative comparison of newly emerged algorithms driven by the maturity of deep learning based approaches and clinical drive for resolving finer details of distal airways for early intervention of pulmonary diseases. Thus far, public annotated datasets are extremely limited, hindering the development of data-driven methods and detailed performance evaluation of new algorithms. To provide a benchmark for the medical imaging community, we organized the Multi-site, Multi-domain Airway Tree Modeling (ATM'22), which was held as an official challenge event during the MICCAI 2022 conference. ATM'22 provides large-scale CT scans with detailed pulmonary airway annotation, including 500 CT scans (300 for training, 50 for validation, and 150 for testing). The dataset was collected from different sites and it further included a portion of noisy COVID-19 CTs with ground-glass opacity and consolidation. Twenty-three teams participated in the entire phase of the challenge and the algorithms for the top ten teams are reviewed in this paper. Quantitative and qualitative results revealed that deep learning models embedded with the topological continuity enhancement achieved superior performance in general. ATM'22 challenge holds as an open-call design, the training data and the gold standard evaluation are available upon successful registration via its homepage.},
 bibtype = {article},
 author = {Zhang, Minghui and Wu, Yangqian and Zhang, Hanxiao and Qin, Yulei and Zheng, Hao and Tang, Wen and Arnold, Corey and Pei, Chenhao and Yu, Pengxin and Nan, Yang and Yang, Guang and Walsh, Simon and Marshall, Dominic C. and Komorowski, Matthieu and Wang, Puyang and Guo, Dazhou and Jin, Dakai and Wu, Ya'nan and Zhao, Shuiqing and Chang, Runsheng and Zhang, Boyu and Lv, Xing and Qayyum, Abdul and Mazher, Moona and Su, Qi and Wu, Yonghuang and Liu, Ying'ao and Zhu, Yufei and Yang, Jiancheng and Pakzad, Ashkan and Rangelov, Bojidar and Estepar, Raul San Jose and Espinosa, Carlos Cano and Sun, Jiayuan and Yang, Guang-Zhong and Gu, Yun},
 journal = {arXiv preprint arXiv:2303.05745}
}

{"_id":"2qWqTAEhj3pAm5ZEe","bibbaseid":"zhang-wu-zhang-qin-zheng-tang-arnold-pei-etal-multisitemultidomainairwaytreemodelingatm22apublicbenchmarkforpulmonaryairwaysegmentation-2023","author_short":["Zhang, M.","Wu, Y.","Zhang, H.","Qin, Y.","Zheng, H.","Tang, W.","Arnold, C.","Pei, C.","Yu, P.","Nan, Y.","Yang, G.","Walsh, S.","Marshall, D., C.","Komorowski, M.","Wang, P.","Guo, D.","Jin, D.","Wu, Y.","Zhao, S.","Chang, R.","Zhang, B.","Lv, X.","Qayyum, A.","Mazher, M.","Su, Q.","Wu, Y.","Liu, Y.","Zhu, Y.","Yang, J.","Pakzad, A.","Rangelov, B.","Estepar, R., S., J.","Espinosa, C., C.","Sun, J.","Yang, G.","Gu, Y."],"bibdata":{"title":"Multi-site, Multi-domain Airway Tree Modeling (ATM'22): A Public Benchmark for Pulmonary Airway Segmentation","type":"article","year":"2023","keywords":"methods,pulmonary airway segmen-,tation,topological prior knowledge,traditional and deep-learning","websites":"http://arxiv.org/abs/2303.05745","id":"7ede210d-4e55-3f76-a81d-eb2dd5b05186","created":"2024-01-13T07:02:56.915Z","file_attached":"true","profile_id":"d5b9f595-4104-33d2-b640-e84dee1b7ded","last_modified":"2024-01-13T08:14:19.400Z","read":false,"starred":false,"authored":"true","confirmed":"true","hidden":false,"private_publication":false,"abstract":"Open international challenges are becoming the de facto standard for assessing computer vision and image analysis algorithms. In recent years, new methods have extended the reach of pulmonary airway segmentation that is closer to the limit of image resolution. Since EXACT'09 pulmonary airway segmentation, limited effort has been directed to quantitative comparison of newly emerged algorithms driven by the maturity of deep learning based approaches and clinical drive for resolving finer details of distal airways for early intervention of pulmonary diseases. Thus far, public annotated datasets are extremely limited, hindering the development of data-driven methods and detailed performance evaluation of new algorithms. To provide a benchmark for the medical imaging community, we organized the Multi-site, Multi-domain Airway Tree Modeling (ATM'22), which was held as an official challenge event during the MICCAI 2022 conference. ATM'22 provides large-scale CT scans with detailed pulmonary airway annotation, including 500 CT scans (300 for training, 50 for validation, and 150 for testing). The dataset was collected from different sites and it further included a portion of noisy COVID-19 CTs with ground-glass opacity and consolidation. Twenty-three teams participated in the entire phase of the challenge and the algorithms for the top ten teams are reviewed in this paper. Quantitative and qualitative results revealed that deep learning models embedded with the topological continuity enhancement achieved superior performance in general. ATM'22 challenge holds as an open-call design, the training data and the gold standard evaluation are available upon successful registration via its homepage.","bibtype":"article","author":"Zhang, Minghui and Wu, Yangqian and Zhang, Hanxiao and Qin, Yulei and Zheng, Hao and Tang, Wen and Arnold, Corey and Pei, Chenhao and Yu, Pengxin and Nan, Yang and Yang, Guang and Walsh, Simon and Marshall, Dominic C. and Komorowski, Matthieu and Wang, Puyang and Guo, Dazhou and Jin, Dakai and Wu, Ya'nan and Zhao, Shuiqing and Chang, Runsheng and Zhang, Boyu and Lv, Xing and Qayyum, Abdul and Mazher, Moona and Su, Qi and Wu, Yonghuang and Liu, Ying'ao and Zhu, Yufei and Yang, Jiancheng and Pakzad, Ashkan and Rangelov, Bojidar and Estepar, Raul San Jose and Espinosa, Carlos Cano and Sun, Jiayuan and Yang, Guang-Zhong and Gu, Yun","journal":"arXiv preprint arXiv:2303.05745","bibtex":"@article{\n title = {Multi-site, Multi-domain Airway Tree Modeling (ATM'22): A Public Benchmark for Pulmonary Airway Segmentation},\n type = {article},\n year = {2023},\n keywords = {methods,pulmonary airway segmen-,tation,topological prior knowledge,traditional and deep-learning},\n websites = {http://arxiv.org/abs/2303.05745},\n id = {7ede210d-4e55-3f76-a81d-eb2dd5b05186},\n created = {2024-01-13T07:02:56.915Z},\n file_attached = {true},\n profile_id = {d5b9f595-4104-33d2-b640-e84dee1b7ded},\n last_modified = {2024-01-13T08:14:19.400Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {Open international challenges are becoming the de facto standard for assessing computer vision and image analysis algorithms. In recent years, new methods have extended the reach of pulmonary airway segmentation that is closer to the limit of image resolution. Since EXACT'09 pulmonary airway segmentation, limited effort has been directed to quantitative comparison of newly emerged algorithms driven by the maturity of deep learning based approaches and clinical drive for resolving finer details of distal airways for early intervention of pulmonary diseases. Thus far, public annotated datasets are extremely limited, hindering the development of data-driven methods and detailed performance evaluation of new algorithms. To provide a benchmark for the medical imaging community, we organized the Multi-site, Multi-domain Airway Tree Modeling (ATM'22), which was held as an official challenge event during the MICCAI 2022 conference. ATM'22 provides large-scale CT scans with detailed pulmonary airway annotation, including 500 CT scans (300 for training, 50 for validation, and 150 for testing). The dataset was collected from different sites and it further included a portion of noisy COVID-19 CTs with ground-glass opacity and consolidation. Twenty-three teams participated in the entire phase of the challenge and the algorithms for the top ten teams are reviewed in this paper. Quantitative and qualitative results revealed that deep learning models embedded with the topological continuity enhancement achieved superior performance in general. ATM'22 challenge holds as an open-call design, the training data and the gold standard evaluation are available upon successful registration via its homepage.},\n bibtype = {article},\n author = {Zhang, Minghui and Wu, Yangqian and Zhang, Hanxiao and Qin, Yulei and Zheng, Hao and Tang, Wen and Arnold, Corey and Pei, Chenhao and Yu, Pengxin and Nan, Yang and Yang, Guang and Walsh, Simon and Marshall, Dominic C. and Komorowski, Matthieu and Wang, Puyang and Guo, Dazhou and Jin, Dakai and Wu, Ya'nan and Zhao, Shuiqing and Chang, Runsheng and Zhang, Boyu and Lv, Xing and Qayyum, Abdul and Mazher, Moona and Su, Qi and Wu, Yonghuang and Liu, Ying'ao and Zhu, Yufei and Yang, Jiancheng and Pakzad, Ashkan and Rangelov, Bojidar and Estepar, Raul San Jose and Espinosa, Carlos Cano and Sun, Jiayuan and Yang, Guang-Zhong and Gu, Yun},\n journal = {arXiv preprint arXiv:2303.05745}\n}","author_short":["Zhang, M.","Wu, Y.","Zhang, H.","Qin, Y.","Zheng, H.","Tang, W.","Arnold, C.","Pei, C.","Yu, P.","Nan, Y.","Yang, G.","Walsh, S.","Marshall, D., C.","Komorowski, M.","Wang, P.","Guo, D.","Jin, D.","Wu, Y.","Zhao, S.","Chang, R.","Zhang, B.","Lv, X.","Qayyum, A.","Mazher, M.","Su, Q.","Wu, Y.","Liu, Y.","Zhu, Y.","Yang, J.","Pakzad, A.","Rangelov, B.","Estepar, R., S., J.","Espinosa, C., C.","Sun, J.","Yang, G.","Gu, Y."],"urls":{"Paper":"https://bibbase.org/service/mendeley/d5b9f595-4104-33d2-b640-e84dee1b7ded/file/65450fa4-2f4c-e2a3-18e0-952316ddba4b/230305745.pdf.pdf","Website":"http://arxiv.org/abs/2303.05745"},"biburl":"https://bibbase.org/service/mendeley/d5b9f595-4104-33d2-b640-e84dee1b7ded","bibbaseid":"zhang-wu-zhang-qin-zheng-tang-arnold-pei-etal-multisitemultidomainairwaytreemodelingatm22apublicbenchmarkforpulmonaryairwaysegmentation-2023","role":"author","keyword":["methods","pulmonary airway segmen-","tation","topological prior knowledge","traditional and deep-learning"],"metadata":{"authorlinks":{}}},"bibtype":"article","biburl":"https://bibbase.org/service/mendeley/d5b9f595-4104-33d2-b640-e84dee1b7ded","dataSources":["ya2CyA73rpZseyrZ8","2252seNhipfTmjEBQ"],"keywords":["methods","pulmonary airway segmen-","tation","topological prior knowledge","traditional and deep-learning"],"search_terms":["multi","site","multi","domain","airway","tree","modeling","atm","public","benchmark","pulmonary","airway","segmentation","zhang","wu","zhang","qin","zheng","tang","arnold","pei","yu","nan","yang","walsh","marshall","komorowski","wang","guo","jin","wu","zhao","chang","zhang","lv","qayyum","mazher","su","wu","liu","zhu","yang","pakzad","rangelov","estepar","espinosa","sun","yang","gu"],"title":"Multi-site, Multi-domain Airway Tree Modeling (ATM'22): A Public Benchmark for Pulmonary Airway Segmentation","year":2023}