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Automatic airway segmentation from computed tomography using robust and efficient 3-D convolutional neural networks
This paper presents a fully automatic and end-to-end optimised airway segmentation method for thoracic computed tomography, based on the U-Net architecture. We use a simple and low-memory 3D U-Net as backbone, which allows the method to process large 3D image patches, often comprising full lungs, in...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8346579/ https://www.ncbi.nlm.nih.gov/pubmed/34362949 http://dx.doi.org/10.1038/s41598-021-95364-1 |
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author | Garcia-Uceda, Antonio Selvan, Raghavendra Saghir, Zaigham Tiddens, Harm A. W. M. de Bruijne, Marleen |
author_facet | Garcia-Uceda, Antonio Selvan, Raghavendra Saghir, Zaigham Tiddens, Harm A. W. M. de Bruijne, Marleen |
author_sort | Garcia-Uceda, Antonio |
collection | PubMed |
description | This paper presents a fully automatic and end-to-end optimised airway segmentation method for thoracic computed tomography, based on the U-Net architecture. We use a simple and low-memory 3D U-Net as backbone, which allows the method to process large 3D image patches, often comprising full lungs, in a single pass through the network. This makes the method simple, robust and efficient. We validated the proposed method on three datasets with very different characteristics and various airway abnormalities: (1) a dataset of pediatric patients including subjects with cystic fibrosis, (2) a subset of the Danish Lung Cancer Screening Trial, including subjects with chronic obstructive pulmonary disease, and (3) the EXACT’09 public dataset. We compared our method with other state-of-the-art airway segmentation methods, including relevant learning-based methods in the literature evaluated on the EXACT’09 data. We show that our method can extract highly complete airway trees with few false positive errors, on scans from both healthy and diseased subjects, and also that the method generalizes well across different datasets. On the EXACT’09 test set, our method achieved the second highest sensitivity score among all methods that reported good specificity. |
format | Online Article Text |
id | pubmed-8346579 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-83465792021-08-10 Automatic airway segmentation from computed tomography using robust and efficient 3-D convolutional neural networks Garcia-Uceda, Antonio Selvan, Raghavendra Saghir, Zaigham Tiddens, Harm A. W. M. de Bruijne, Marleen Sci Rep Article This paper presents a fully automatic and end-to-end optimised airway segmentation method for thoracic computed tomography, based on the U-Net architecture. We use a simple and low-memory 3D U-Net as backbone, which allows the method to process large 3D image patches, often comprising full lungs, in a single pass through the network. This makes the method simple, robust and efficient. We validated the proposed method on three datasets with very different characteristics and various airway abnormalities: (1) a dataset of pediatric patients including subjects with cystic fibrosis, (2) a subset of the Danish Lung Cancer Screening Trial, including subjects with chronic obstructive pulmonary disease, and (3) the EXACT’09 public dataset. We compared our method with other state-of-the-art airway segmentation methods, including relevant learning-based methods in the literature evaluated on the EXACT’09 data. We show that our method can extract highly complete airway trees with few false positive errors, on scans from both healthy and diseased subjects, and also that the method generalizes well across different datasets. On the EXACT’09 test set, our method achieved the second highest sensitivity score among all methods that reported good specificity. Nature Publishing Group UK 2021-08-06 /pmc/articles/PMC8346579/ /pubmed/34362949 http://dx.doi.org/10.1038/s41598-021-95364-1 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Garcia-Uceda, Antonio Selvan, Raghavendra Saghir, Zaigham Tiddens, Harm A. W. M. de Bruijne, Marleen Automatic airway segmentation from computed tomography using robust and efficient 3-D convolutional neural networks |
title | Automatic airway segmentation from computed tomography using robust and efficient 3-D convolutional neural networks |
title_full | Automatic airway segmentation from computed tomography using robust and efficient 3-D convolutional neural networks |
title_fullStr | Automatic airway segmentation from computed tomography using robust and efficient 3-D convolutional neural networks |
title_full_unstemmed | Automatic airway segmentation from computed tomography using robust and efficient 3-D convolutional neural networks |
title_short | Automatic airway segmentation from computed tomography using robust and efficient 3-D convolutional neural networks |
title_sort | automatic airway segmentation from computed tomography using robust and efficient 3-d convolutional neural networks |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8346579/ https://www.ncbi.nlm.nih.gov/pubmed/34362949 http://dx.doi.org/10.1038/s41598-021-95364-1 |
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