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research article

Multi-site, Multi-domain Airway Tree Modeling

Zhang, Minghui
•
Wu, Yangqian
•
Zhang, Hanxiao
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December 1, 2023
Medical Image Analysis

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/).

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Type
research article
DOI
10.1016/j.media.2023.102957
Web of Science ID

WOS:001165199200001

Author(s)
Zhang, Minghui
Wu, Yangqian
Zhang, Hanxiao
Qin, Yulei
Zheng, Hao
Tang, Wen
Arnold, Corey
Pei, Chenhao
Yu, Pengxin
Nan, Yang
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Date Issued

2023-12-01

Publisher

Elsevier

Published in
Medical Image Analysis
Volume

90

Article Number

102957

Subjects

Technology

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Life Sciences & Biomedicine

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Pulmonary Airway Segmentation

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Traditional And Deep-Learning Methods

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Topological Prior Knowledge

Editorial or Peer reviewed

REVIEWED

Written at

EPFL

EPFL units
CVLAB  
FunderGrant Number

Open Funding of Zhejiang Laboratory, China

2021KH0AB03

Shanghai Sailing Program, China

20YF1420800

NSFC, China

62003208

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Available on Infoscience
March 18, 2024
Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/206445
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