Deep learning methods allow fully automated segmentation of metacarpal bones to quantify volumetric bone mineral density

Abstract Arthritis patients develop hand bone loss, which leads to destruction and functional impairment of the affected joints. High resolution peripheral quantitative computed tomography (HR-pQCT) allows the quantification of volumetric bone mineral density (vBMD) and bone microstructure in vivo w...

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Autores principales:	Lukas Folle, Timo Meinderink, David Simon, Anna-Maria Liphardt, Gerhard Krönke, Georg Schett, Arnd Kleyer, Andreas Maier
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/fe15b3d6fc804cfc8e6404bf83ab7a66
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spelling	oai:doaj.org-article:fe15b3d6fc804cfc8e6404bf83ab7a662021-12-02T15:38:10ZDeep learning methods allow fully automated segmentation of metacarpal bones to quantify volumetric bone mineral density10.1038/s41598-021-89111-92045-2322https://doaj.org/article/fe15b3d6fc804cfc8e6404bf83ab7a662021-05-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-89111-9https://doaj.org/toc/2045-2322Abstract Arthritis patients develop hand bone loss, which leads to destruction and functional impairment of the affected joints. High resolution peripheral quantitative computed tomography (HR-pQCT) allows the quantification of volumetric bone mineral density (vBMD) and bone microstructure in vivo with an isotropic voxel size of 82 micrometres. However, image-processing to obtain bone characteristics is a time-consuming process as it requires semi-automatic segmentation of the bone. In this work, a fully automatic vBMD measurement pipeline for the metacarpal (MC) bone using deep learning methods is introduced. Based on a dataset of HR-pQCT volumes with MC measurements for 541 patients with arthritis, a segmentation network is trained. The best network achieves an intersection over union as high as 0.94 and a Dice similarity coefficient of 0.97 while taking only 33 s to process a whole patient yielding a speedup between 2.5 and 4.0 for the whole workflow. Strong correlation between the vBMD measurements of the expert and of the automatic pipeline are achieved for the average bone density with 0.999 (Pearson) and 0.996 (Spearman’s rank) with $$p < 0.001$$ p < 0.001 for all correlations. A qualitative assessment of the network predictions and the manual annotations yields a 65.9% probability that the expert favors the network predictions. Further, the steps to integrate the pipeline into the clinical workflow are shown. In order to make these workflow improvements available to others, we openly share the code of this work.Lukas FolleTimo MeinderinkDavid SimonAnna-Maria LiphardtGerhard KrönkeGeorg SchettArnd KleyerAndreas MaierNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-9 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Lukas Folle Timo Meinderink David Simon Anna-Maria Liphardt Gerhard Krönke Georg Schett Arnd Kleyer Andreas Maier Deep learning methods allow fully automated segmentation of metacarpal bones to quantify volumetric bone mineral density
description	Abstract Arthritis patients develop hand bone loss, which leads to destruction and functional impairment of the affected joints. High resolution peripheral quantitative computed tomography (HR-pQCT) allows the quantification of volumetric bone mineral density (vBMD) and bone microstructure in vivo with an isotropic voxel size of 82 micrometres. However, image-processing to obtain bone characteristics is a time-consuming process as it requires semi-automatic segmentation of the bone. In this work, a fully automatic vBMD measurement pipeline for the metacarpal (MC) bone using deep learning methods is introduced. Based on a dataset of HR-pQCT volumes with MC measurements for 541 patients with arthritis, a segmentation network is trained. The best network achieves an intersection over union as high as 0.94 and a Dice similarity coefficient of 0.97 while taking only 33 s to process a whole patient yielding a speedup between 2.5 and 4.0 for the whole workflow. Strong correlation between the vBMD measurements of the expert and of the automatic pipeline are achieved for the average bone density with 0.999 (Pearson) and 0.996 (Spearman’s rank) with $$p < 0.001$$ p < 0.001 for all correlations. A qualitative assessment of the network predictions and the manual annotations yields a 65.9% probability that the expert favors the network predictions. Further, the steps to integrate the pipeline into the clinical workflow are shown. In order to make these workflow improvements available to others, we openly share the code of this work.
format	article
author	Lukas Folle Timo Meinderink David Simon Anna-Maria Liphardt Gerhard Krönke Georg Schett Arnd Kleyer Andreas Maier
author_facet	Lukas Folle Timo Meinderink David Simon Anna-Maria Liphardt Gerhard Krönke Georg Schett Arnd Kleyer Andreas Maier
author_sort	Lukas Folle
title	Deep learning methods allow fully automated segmentation of metacarpal bones to quantify volumetric bone mineral density
title_short	Deep learning methods allow fully automated segmentation of metacarpal bones to quantify volumetric bone mineral density
title_full	Deep learning methods allow fully automated segmentation of metacarpal bones to quantify volumetric bone mineral density
title_fullStr	Deep learning methods allow fully automated segmentation of metacarpal bones to quantify volumetric bone mineral density
title_full_unstemmed	Deep learning methods allow fully automated segmentation of metacarpal bones to quantify volumetric bone mineral density
title_sort	deep learning methods allow fully automated segmentation of metacarpal bones to quantify volumetric bone mineral density
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/fe15b3d6fc804cfc8e6404bf83ab7a66
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_version_	1718386210562375680

Deep learning methods allow fully automated segmentation of metacarpal bones to quantify volumetric bone mineral density

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