Creating a training set for artificial intelligence from initial segmentations of airways

Abstract Airways segmentation is important for research about pulmonary disease but require a large amount of time by trained specialists. We used an openly available software to improve airways segmentations obtained from an artificial intelligence (AI) tool and retrained the tool to get a better p...

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Autores principales:	Ivan Dudurych, Antonio Garcia-Uceda, Zaigham Saghir, Harm A. W. M. Tiddens, Rozemarijn Vliegenthart, Marleen de Bruijne
Formato:	article
Lenguaje:	EN
Publicado:	SpringerOpen 2021
Materias:	Artificial intelligence Image processing (computer-assisted) Respiratory system Thorax Tomography (x-ray computed) Medical physics. Medical radiology. Nuclear medicine R895-920
Acceso en línea:	https://doaj.org/article/31bfd843b25f4eeaace52d7880566288
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spelling	oai:doaj.org-article:31bfd843b25f4eeaace52d78805662882021-12-05T12:10:48ZCreating a training set for artificial intelligence from initial segmentations of airways10.1186/s41747-021-00247-92509-9280https://doaj.org/article/31bfd843b25f4eeaace52d78805662882021-11-01T00:00:00Zhttps://doi.org/10.1186/s41747-021-00247-9https://doaj.org/toc/2509-9280Abstract Airways segmentation is important for research about pulmonary disease but require a large amount of time by trained specialists. We used an openly available software to improve airways segmentations obtained from an artificial intelligence (AI) tool and retrained the tool to get a better performance. Fifteen initial airway segmentations from low-dose chest computed tomography scans were obtained with a 3D-Unet AI tool previously trained on Danish Lung Cancer Screening Trial and Erasmus-MC Sophia datasets. Segmentations were manually corrected in 3D Slicer. The corrected airway segmentations were used to retrain the 3D-Unet. Airway measurements were automatically obtained and included count, airway length and luminal diameter per generation from the segmentations. Correcting segmentations required 2–4 h per scan. Manually corrected segmentations had more branches (p < 0.001), longer airways (p < 0.001) and smaller luminal diameters (p = 0.004) than initial segmentations. Segmentations from retrained 3D-Unets trended towards more branches and longer airways compared to the initial segmentations. The largest changes were seen in airways from 6th generation onwards. Manual correction results in significantly improved segmentations and is potentially a useful and time-efficient method to improve the AI tool performance on a specific hospital or research dataset.Ivan DudurychAntonio Garcia-UcedaZaigham SaghirHarm A. W. M. TiddensRozemarijn VliegenthartMarleen de BruijneSpringerOpenarticleArtificial intelligenceImage processing (computer-assisted)Respiratory systemThoraxTomography (x-ray computed)Medical physics. Medical radiology. Nuclear medicineR895-920ENEuropean Radiology Experimental, Vol 5, Iss 1, Pp 1-7 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Artificial intelligence Image processing (computer-assisted) Respiratory system Thorax Tomography (x-ray computed) Medical physics. Medical radiology. Nuclear medicine R895-920
spellingShingle	Artificial intelligence Image processing (computer-assisted) Respiratory system Thorax Tomography (x-ray computed) Medical physics. Medical radiology. Nuclear medicine R895-920 Ivan Dudurych Antonio Garcia-Uceda Zaigham Saghir Harm A. W. M. Tiddens Rozemarijn Vliegenthart Marleen de Bruijne Creating a training set for artificial intelligence from initial segmentations of airways
description	Abstract Airways segmentation is important for research about pulmonary disease but require a large amount of time by trained specialists. We used an openly available software to improve airways segmentations obtained from an artificial intelligence (AI) tool and retrained the tool to get a better performance. Fifteen initial airway segmentations from low-dose chest computed tomography scans were obtained with a 3D-Unet AI tool previously trained on Danish Lung Cancer Screening Trial and Erasmus-MC Sophia datasets. Segmentations were manually corrected in 3D Slicer. The corrected airway segmentations were used to retrain the 3D-Unet. Airway measurements were automatically obtained and included count, airway length and luminal diameter per generation from the segmentations. Correcting segmentations required 2–4 h per scan. Manually corrected segmentations had more branches (p < 0.001), longer airways (p < 0.001) and smaller luminal diameters (p = 0.004) than initial segmentations. Segmentations from retrained 3D-Unets trended towards more branches and longer airways compared to the initial segmentations. The largest changes were seen in airways from 6th generation onwards. Manual correction results in significantly improved segmentations and is potentially a useful and time-efficient method to improve the AI tool performance on a specific hospital or research dataset.
format	article
author	Ivan Dudurych Antonio Garcia-Uceda Zaigham Saghir Harm A. W. M. Tiddens Rozemarijn Vliegenthart Marleen de Bruijne
author_facet	Ivan Dudurych Antonio Garcia-Uceda Zaigham Saghir Harm A. W. M. Tiddens Rozemarijn Vliegenthart Marleen de Bruijne
author_sort	Ivan Dudurych
title	Creating a training set for artificial intelligence from initial segmentations of airways
title_short	Creating a training set for artificial intelligence from initial segmentations of airways
title_full	Creating a training set for artificial intelligence from initial segmentations of airways
title_fullStr	Creating a training set for artificial intelligence from initial segmentations of airways
title_full_unstemmed	Creating a training set for artificial intelligence from initial segmentations of airways
title_sort	creating a training set for artificial intelligence from initial segmentations of airways
publisher	SpringerOpen
publishDate	2021
url	https://doaj.org/article/31bfd843b25f4eeaace52d7880566288
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Creating a training set for artificial intelligence from initial segmentations of airways

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