Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Garcia-Uceda, Antonio, Selvan, Raghavendra, Saghir, Zaigham, Tiddens, Harm A. W. M., de Bruijne, Marleen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
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
_version_ 1783734906428325888
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
work_keys_str_mv AT garciaucedaantonio automaticairwaysegmentationfromcomputedtomographyusingrobustandefficient3dconvolutionalneuralnetworks
AT selvanraghavendra automaticairwaysegmentationfromcomputedtomographyusingrobustandefficient3dconvolutionalneuralnetworks
AT saghirzaigham automaticairwaysegmentationfromcomputedtomographyusingrobustandefficient3dconvolutionalneuralnetworks
AT tiddensharmawm automaticairwaysegmentationfromcomputedtomographyusingrobustandefficient3dconvolutionalneuralnetworks
AT debruijnemarleen automaticairwaysegmentationfromcomputedtomographyusingrobustandefficient3dconvolutionalneuralnetworks