دورية أكاديمية
Multi-site, Multi-domain Airway Tree Modeling.
| العنوان: | Multi-site, Multi-domain Airway Tree Modeling. |
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| المؤلفون: | Zhang M; Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, 200240, China; Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai, 200240, China; Department of Automation, Shanghai Jiao Tong University, Shanghai, 200240, China., Wu Y; Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, 200240, China; Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai, 200240, China; Department of Automation, Shanghai Jiao Tong University, Shanghai, 200240, China., Zhang H; Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, 200240, China., Qin Y; Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, 200240, China., Zheng H; Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, 200240, China., Tang W; InferVision Medical Technology Co., Ltd., Beijing, China., Arnold C; University of California, Los Angeles, CA, USA., Pei C; InferVision Medical Technology Co., Ltd., Beijing, China., Yu P; InferVision Medical Technology Co., Ltd., Beijing, China., Nan Y; Imperial College London, London, UK., Yang G; Imperial College London, London, UK., Walsh S; Imperial College London, London, UK., Marshall DC; Department of Surgery and Cancer, Imperial College London, London, UK., Komorowski M; Department of Surgery and Cancer, Imperial College London, London, UK., Wang P; Alibaba DAMO Academy, 969 West Wen Yi Road, Hangzhou, Zhejiang, China., Guo D; Alibaba DAMO Academy USA, 860 Washington Street, 8F, NY, USA., Jin D; Alibaba DAMO Academy USA, 860 Washington Street, 8F, NY, USA., Wu Y; College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China., Zhao S; College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China., Chang R; College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China., Zhang B; A.I R&D Center, Sanmed Biotech Inc., No. 266 Tongchang Road, Xiangzhou District, Zhuhai, Guangdong, China., Lu X; A.I R&D Center, Sanmed Biotech Inc., T220 Trade st. SanDiego, CA, USA., Qayyum A; ENIB, UMR CNRS 6285 LabSTICC, Brest, 29238, France., Mazher M; Department of Computer Engineering and Mathematics, University Rovira I Virgili, Tarragona, Spain., Su Q; Shanghai Jiao Tong University, Shanghai, China., Wu Y; School of Information Science and Technology, Fudan University, Shanghai, China., Liu Y; University of Science and Technology of China, Hefei, Anhui, China., Zhu Y; Dianei Technology, Shanghai, China., Yang J; Dianei Technology, Shanghai, China; EPFL, Lausanne, Switzerland., Pakzad A; Medical Physics and Biomedical Engineering Department, University College London, London, UK., Rangelov B; Center for Medical Image Computing, University College London, London, UK., Estepar RSJ; Brigham and Women's Hospital, Harvard Medical School, Somerville, MA 02145, USA., Espinosa CC; Brigham and Women's Hospital, Harvard Medical School, Somerville, MA 02145, USA., Sun J; Department of Respiratory and Critical Care Medicine, Department of Respiratory Endoscopy, Shanghai Chest Hospital, Shanghai, China. Electronic address: sunjy1976@sjtu.edu.cn., Yang GZ; Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, 200240, China. Electronic address: gzyang@sjtu.edu.cn., Gu Y; Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, 200240, China; Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai, 200240, China; Department of Automation, Shanghai Jiao Tong University, Shanghai, 200240, China. Electronic address: geron762@sjtu.edu.cn. |
| المصدر: | Medical image analysis [Med Image Anal] 2023 Dec; Vol. 90, pp. 102957. Date of Electronic Publication: 2023 Sep 09. |
| نوع المنشور: | Journal Article |
| اللغة: | English |
| بيانات الدورية: | Publisher: Elsevier Country of Publication: Netherlands NLM ID: 9713490 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1361-8423 (Electronic) Linking ISSN: 13618415 NLM ISO Abbreviation: Med Image Anal Subsets: MEDLINE |
| أسماء مطبوعة: | Publication: Amsterdam : Elsevier Original Publication: London : Oxford University Press, [1996- |
| مواضيع طبية MeSH: | Trees* , Lung Diseases*, Humans ; Tomography, X-Ray Computed/methods ; Image Processing, Computer-Assisted/methods ; Algorithms ; Lung/diagnostic imaging |
| مستخلص: | 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 the quantitative comparison of newly emerged algorithms driven by the maturity of deep learning based approaches and extensive clinical efforts 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. Both 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 (https://atm22.grand-challenge.org/). Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. (Copyright © 2023. Published by Elsevier B.V.) |
| معلومات مُعتمدة: | MR/V023799/1 United Kingdom MRC_ Medical Research Council |
| فهرسة مساهمة: | Keywords: Pulmonary airway segmentation; Topological prior knowledge; Traditional and deep-learning methods |
| تواريخ الأحداث: | Date Created: 20230916 Date Completed: 20240215 Latest Revision: 20240215 |
| رمز التحديث: | 20240215 |
| DOI: | 10.1016/j.media.2023.102957 |
| PMID: | 37716199 |
| قاعدة البيانات: | MEDLINE |
Full Text Finder | تدمد: | 1361-8423 |
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| DOI: | 10.1016/j.media.2023.102957 |