A deep learning method for automatic segmentation of the bony orbit in MRI and CT images

Abstract This paper proposes a fully automatic method to segment the inner boundary of the bony orbit in two different image modalities: magnetic resonance imaging (MRI) and computed tomography (CT). The method, based on a deep learning architecture, uses two fully convolutional neural networks in s...

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Autores principales:	Jared Hamwood, Beat Schmutz, Michael J. Collins, Mark C. Allenby, David Alonso-Caneiro
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/8c592a3aa8dc4fbfa6b9d3d70ef56fff
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id	oai:doaj.org-article:8c592a3aa8dc4fbfa6b9d3d70ef56fff
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spelling	oai:doaj.org-article:8c592a3aa8dc4fbfa6b9d3d70ef56fff2021-12-02T18:18:59ZA deep learning method for automatic segmentation of the bony orbit in MRI and CT images10.1038/s41598-021-93227-32045-2322https://doaj.org/article/8c592a3aa8dc4fbfa6b9d3d70ef56fff2021-07-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-93227-3https://doaj.org/toc/2045-2322Abstract This paper proposes a fully automatic method to segment the inner boundary of the bony orbit in two different image modalities: magnetic resonance imaging (MRI) and computed tomography (CT). The method, based on a deep learning architecture, uses two fully convolutional neural networks in series followed by a graph-search method to generate a boundary for the orbit. When compared to human performance for segmentation of both CT and MRI data, the proposed method achieves high Dice coefficients on both orbit and background, with scores of 0.813 and 0.975 in CT images and 0.930 and 0.995 in MRI images, showing a high degree of agreement with a manual segmentation by a human expert. Given the volumetric characteristics of these imaging modalities and the complexity and time-consuming nature of the segmentation of the orbital region in the human skull, it is often impractical to manually segment these images. Thus, the proposed method provides a valid clinical and research tool that performs similarly to the human observer.Jared HamwoodBeat SchmutzMichael J. CollinsMark C. AllenbyDavid Alonso-CaneiroNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Jared Hamwood Beat Schmutz Michael J. Collins Mark C. Allenby David Alonso-Caneiro A deep learning method for automatic segmentation of the bony orbit in MRI and CT images
description	Abstract This paper proposes a fully automatic method to segment the inner boundary of the bony orbit in two different image modalities: magnetic resonance imaging (MRI) and computed tomography (CT). The method, based on a deep learning architecture, uses two fully convolutional neural networks in series followed by a graph-search method to generate a boundary for the orbit. When compared to human performance for segmentation of both CT and MRI data, the proposed method achieves high Dice coefficients on both orbit and background, with scores of 0.813 and 0.975 in CT images and 0.930 and 0.995 in MRI images, showing a high degree of agreement with a manual segmentation by a human expert. Given the volumetric characteristics of these imaging modalities and the complexity and time-consuming nature of the segmentation of the orbital region in the human skull, it is often impractical to manually segment these images. Thus, the proposed method provides a valid clinical and research tool that performs similarly to the human observer.
format	article
author	Jared Hamwood Beat Schmutz Michael J. Collins Mark C. Allenby David Alonso-Caneiro
author_facet	Jared Hamwood Beat Schmutz Michael J. Collins Mark C. Allenby David Alonso-Caneiro
author_sort	Jared Hamwood
title	A deep learning method for automatic segmentation of the bony orbit in MRI and CT images
title_short	A deep learning method for automatic segmentation of the bony orbit in MRI and CT images
title_full	A deep learning method for automatic segmentation of the bony orbit in MRI and CT images
title_fullStr	A deep learning method for automatic segmentation of the bony orbit in MRI and CT images
title_full_unstemmed	A deep learning method for automatic segmentation of the bony orbit in MRI and CT images
title_sort	deep learning method for automatic segmentation of the bony orbit in mri and ct images
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/8c592a3aa8dc4fbfa6b9d3d70ef56fff
work_keys_str_mv	AT jaredhamwood adeeplearningmethodforautomaticsegmentationofthebonyorbitinmriandctimages AT beatschmutz adeeplearningmethodforautomaticsegmentationofthebonyorbitinmriandctimages AT michaeljcollins adeeplearningmethodforautomaticsegmentationofthebonyorbitinmriandctimages AT markcallenby adeeplearningmethodforautomaticsegmentationofthebonyorbitinmriandctimages AT davidalonsocaneiro adeeplearningmethodforautomaticsegmentationofthebonyorbitinmriandctimages AT jaredhamwood deeplearningmethodforautomaticsegmentationofthebonyorbitinmriandctimages AT beatschmutz deeplearningmethodforautomaticsegmentationofthebonyorbitinmriandctimages AT michaeljcollins deeplearningmethodforautomaticsegmentationofthebonyorbitinmriandctimages AT markcallenby deeplearningmethodforautomaticsegmentationofthebonyorbitinmriandctimages AT davidalonsocaneiro deeplearningmethodforautomaticsegmentationofthebonyorbitinmriandctimages
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A deep learning method for automatic segmentation of the bony orbit in MRI and CT images

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