Detecting the pulmonary trunk in CT scout views using deep learning

Abstract For CT pulmonary angiograms, a scout view obtained in anterior–posterior projection is usually used for planning. For bolus tracking the radiographer manually locates a position in the CT scout view where the pulmonary trunk will be visible in an axial CT pre-scan. We automate the task of l...

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Autores principales:	Aydin Demircioğlu, Magdalena Charis Stein, Moon-Sung Kim, Henrike Geske, Anton S. Quinsten, Sebastian Blex, Lale Umutlu, Kai Nassenstein
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/fbea795d393c48c8a30cf0b7e01036ab
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spelling	oai:doaj.org-article:fbea795d393c48c8a30cf0b7e01036ab2021-12-02T15:42:59ZDetecting the pulmonary trunk in CT scout views using deep learning10.1038/s41598-021-89647-w2045-2322https://doaj.org/article/fbea795d393c48c8a30cf0b7e01036ab2021-05-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-89647-whttps://doaj.org/toc/2045-2322Abstract For CT pulmonary angiograms, a scout view obtained in anterior–posterior projection is usually used for planning. For bolus tracking the radiographer manually locates a position in the CT scout view where the pulmonary trunk will be visible in an axial CT pre-scan. We automate the task of localizing the pulmonary trunk in CT scout views by deep learning methods. In 620 eligible CT scout views of 563 patients between March 2003 and February 2020 the region of the pulmonary trunk as well as an optimal slice (“reference standard”) for bolus tracking, in which the pulmonary trunk was clearly visible, was annotated and used to train a U-Net predicting the region of the pulmonary trunk in the CT scout view. The networks’ performance was subsequently evaluated on 239 CT scout views from 213 patients and was compared with the annotations of three radiographers. The network was able to localize the region of the pulmonary trunk with high accuracy, yielding an accuracy of 97.5% of localizing a slice in the region of the pulmonary trunk on the validation cohort. On average, the selected position had a distance of 5.3 mm from the reference standard. Compared to radiographers, using a non-inferiority test (one-sided, paired Wilcoxon rank-sum test) the network performed as well as each radiographer (P < 0.001 in all cases). Automated localization of the region of the pulmonary trunk in CT scout views is possible with high accuracy and is non-inferior to three radiographers.Aydin DemircioğluMagdalena Charis SteinMoon-Sung KimHenrike GeskeAnton S. QuinstenSebastian BlexLale UmutluKai NassensteinNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-7 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Aydin Demircioğlu Magdalena Charis Stein Moon-Sung Kim Henrike Geske Anton S. Quinsten Sebastian Blex Lale Umutlu Kai Nassenstein Detecting the pulmonary trunk in CT scout views using deep learning
description	Abstract For CT pulmonary angiograms, a scout view obtained in anterior–posterior projection is usually used for planning. For bolus tracking the radiographer manually locates a position in the CT scout view where the pulmonary trunk will be visible in an axial CT pre-scan. We automate the task of localizing the pulmonary trunk in CT scout views by deep learning methods. In 620 eligible CT scout views of 563 patients between March 2003 and February 2020 the region of the pulmonary trunk as well as an optimal slice (“reference standard”) for bolus tracking, in which the pulmonary trunk was clearly visible, was annotated and used to train a U-Net predicting the region of the pulmonary trunk in the CT scout view. The networks’ performance was subsequently evaluated on 239 CT scout views from 213 patients and was compared with the annotations of three radiographers. The network was able to localize the region of the pulmonary trunk with high accuracy, yielding an accuracy of 97.5% of localizing a slice in the region of the pulmonary trunk on the validation cohort. On average, the selected position had a distance of 5.3 mm from the reference standard. Compared to radiographers, using a non-inferiority test (one-sided, paired Wilcoxon rank-sum test) the network performed as well as each radiographer (P < 0.001 in all cases). Automated localization of the region of the pulmonary trunk in CT scout views is possible with high accuracy and is non-inferior to three radiographers.
format	article
author	Aydin Demircioğlu Magdalena Charis Stein Moon-Sung Kim Henrike Geske Anton S. Quinsten Sebastian Blex Lale Umutlu Kai Nassenstein
author_facet	Aydin Demircioğlu Magdalena Charis Stein Moon-Sung Kim Henrike Geske Anton S. Quinsten Sebastian Blex Lale Umutlu Kai Nassenstein
author_sort	Aydin Demircioğlu
title	Detecting the pulmonary trunk in CT scout views using deep learning
title_short	Detecting the pulmonary trunk in CT scout views using deep learning
title_full	Detecting the pulmonary trunk in CT scout views using deep learning
title_fullStr	Detecting the pulmonary trunk in CT scout views using deep learning
title_full_unstemmed	Detecting the pulmonary trunk in CT scout views using deep learning
title_sort	detecting the pulmonary trunk in ct scout views using deep learning
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/fbea795d393c48c8a30cf0b7e01036ab
work_keys_str_mv	AT aydindemircioglu detectingthepulmonarytrunkinctscoutviewsusingdeeplearning AT magdalenacharisstein detectingthepulmonarytrunkinctscoutviewsusingdeeplearning AT moonsungkim detectingthepulmonarytrunkinctscoutviewsusingdeeplearning AT henrikegeske detectingthepulmonarytrunkinctscoutviewsusingdeeplearning AT antonsquinsten detectingthepulmonarytrunkinctscoutviewsusingdeeplearning AT sebastianblex detectingthepulmonarytrunkinctscoutviewsusingdeeplearning AT laleumutlu detectingthepulmonarytrunkinctscoutviewsusingdeeplearning AT kainassenstein detectingthepulmonarytrunkinctscoutviewsusingdeeplearning
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Detecting the pulmonary trunk in CT scout views using deep learning

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