Automatic airway segmentation from computed tomography using robust and efficient 3-D convolutional neural networks

Abstract 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...

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Autores principales:	Antonio Garcia-Uceda, Raghavendra Selvan, Zaigham Saghir, Harm A. W. M. Tiddens, Marleen de Bruijne
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/04be7a8e66d846138c66cbc7a117dc5e
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spelling	oai:doaj.org-article:04be7a8e66d846138c66cbc7a117dc5e2021-12-02T16:35:37ZAutomatic airway segmentation from computed tomography using robust and efficient 3-D convolutional neural networks10.1038/s41598-021-95364-12045-2322https://doaj.org/article/04be7a8e66d846138c66cbc7a117dc5e2021-08-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-95364-1https://doaj.org/toc/2045-2322Abstract 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.Antonio Garcia-UcedaRaghavendra SelvanZaigham SaghirHarm A. W. M. TiddensMarleen de BruijneNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-15 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Antonio Garcia-Uceda Raghavendra Selvan Zaigham Saghir Harm A. W. M. Tiddens Marleen de Bruijne Automatic airway segmentation from computed tomography using robust and efficient 3-D convolutional neural networks
description	Abstract 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	article
author	Antonio Garcia-Uceda Raghavendra Selvan Zaigham Saghir Harm A. W. M. Tiddens Marleen de Bruijne
author_facet	Antonio Garcia-Uceda Raghavendra Selvan Zaigham Saghir Harm A. W. M. Tiddens Marleen de Bruijne
author_sort	Antonio Garcia-Uceda
title	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_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_sort	automatic airway segmentation from computed tomography using robust and efficient 3-d convolutional neural networks
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/04be7a8e66d846138c66cbc7a117dc5e
work_keys_str_mv	AT antoniogarciauceda automaticairwaysegmentationfromcomputedtomographyusingrobustandefficient3dconvolutionalneuralnetworks AT raghavendraselvan automaticairwaysegmentationfromcomputedtomographyusingrobustandefficient3dconvolutionalneuralnetworks AT zaighamsaghir automaticairwaysegmentationfromcomputedtomographyusingrobustandefficient3dconvolutionalneuralnetworks AT harmawmtiddens automaticairwaysegmentationfromcomputedtomographyusingrobustandefficient3dconvolutionalneuralnetworks AT marleendebruijne automaticairwaysegmentationfromcomputedtomographyusingrobustandefficient3dconvolutionalneuralnetworks
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Automatic airway segmentation from computed tomography using robust and efficient 3-D convolutional neural networks

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