Zhang, Minghui;
Wu, Yangqian;
Zhang, Hanxiao;
Qin, Yulei;
Zheng, Hao;
Tang, Wen;
Arnold, Corey;
... Gu, Yun; + view all
(2023)
Multi-site, Multi-domain Airway Tree Modeling.
Medical Image Analysis
, 90
, Article 102957. 10.1016/j.media.2023.102957.
(In press).
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Rangelov_2023_Multi-site_Multi-domain_Airway_Tree_Modeling.pdf - Accepted Version Access restricted to UCL open access staff until 10 September 2024. Download (7MB) |
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 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/).
| Type: | Article |
|---|---|
| Title: | Multi-site, Multi-domain Airway Tree Modeling |
| Location: | Netherlands |
| DOI: | 10.1016/j.media.2023.102957 |
| Publisher version: | https://doi.org/10.1016/j.media.2023.102957 |
| Language: | English |
| Additional information: | This version is the author-accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Pulmonary airway segmentation, Topological prior knowledge, Traditional and deep-learning methods |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Med Phys and Biomedical Eng |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10179048 |
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