Detail-sensitive 3D-UNet for pulmonary airway segmentation from CT images-Reference-Cited by-同舟云学术

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Detail-sensitive 3D-UNet for pulmonary airway segmentation from CT images

Published:2024-07-17 Issue: Volume: Page:
ISSN:0140-0118
Container-title:Medical & Biological Engineering & Computing
language:en
Short-container-title:Med Biol Eng Comput

Author:

Zhang Qin,Li Jiajie,Nan Xiangling,Zhang Xiaodong^ORCID

Funder

Science and Technology Planning Project of Guangdong Province

National Natural Science Foundation of China

Guangdong High-level Hospital Construction Fund

Guangdong High-level Hospital Construction Fund Clinical Research Project of Shenzhen Children’s Hospital

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s11517-024-03169-x.pdf

Reference53 articles.

1. Xie M, Liu X, Cao X, Guo M, Li X (2020) Trends in prevalence and incidence of chronic respiratory diseases from 1990 to 2017. Resp Res 21:1–13

2. Maken P, Gupta A, Gupta MK (2023) A systematic review of the techniques for automatic segmentation of the human upper airway using volumetric images. Med Biol Eng Comput 61(8):1901–1927

3. Ke Z, Xu X, Zhou K, Guo J (2023) A scale-aware UNet++ model combined with attentional context supervision and adaptive tversky loss for accurate airway segmentation. Appl Intell 53(15):18138–18154

4. Washko GR, Coxson HO, O’Donnell DE, Aaron SD (2017) CT imaging of chronic obstructive pulmonary disease: insights, disappointments, and promise. The Lancet Respiratory Medicine 5(11):903–908

5. Kuo W, Bruijne M, Petersen J, Nasserinejad K, Ozturk H, Chen Y, Perez-Rovira A, Tiddens HA (2017) Diagnosis of bronchiectasis and airway wall thickening in children with cystic fibrosis: objective airway-artery quantification. Eur Radiol 27:4680–4689

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