Automatic segmentation of inner ear on CT-scan using auto-context convolutional neural network

Abstract Temporal bone CT-scan is a prerequisite in most surgical procedures concerning the ear such as cochlear implants. The 3D vision of inner ear structures is crucial for diagnostic and surgical preplanning purposes. Since clinical CT-scans are acquired at relatively low resolutions, improved p...

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Autores principales:	Raabid Hussain, Alain Lalande, Kibrom Berihu Girum, Caroline Guigou, Alexis Bozorg Grayeli
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/b23d705da78b4a40b68db403c37fc58f
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spelling	oai:doaj.org-article:b23d705da78b4a40b68db403c37fc58f2021-12-02T16:23:14ZAutomatic segmentation of inner ear on CT-scan using auto-context convolutional neural network10.1038/s41598-021-83955-x2045-2322https://doaj.org/article/b23d705da78b4a40b68db403c37fc58f2021-02-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-83955-xhttps://doaj.org/toc/2045-2322Abstract Temporal bone CT-scan is a prerequisite in most surgical procedures concerning the ear such as cochlear implants. The 3D vision of inner ear structures is crucial for diagnostic and surgical preplanning purposes. Since clinical CT-scans are acquired at relatively low resolutions, improved performance can be achieved by registering patient-specific CT images to a high-resolution inner ear model built from accurate 3D segmentations based on micro-CT of human temporal bone specimens. This paper presents a framework based on convolutional neural network for human inner ear segmentation from micro-CT images which can be used to build such a model from an extensive database. The proposed approach employs an auto-context based cascaded 2D U-net architecture with 3D connected component refinement to segment the cochlear scalae, semicircular canals, and the vestibule. The system was formulated on a data set composed of 17 micro-CT from public Hear-EU dataset. A Dice coefficient of 0.90 and Hausdorff distance of 0.74 mm were obtained. The system yielded precise and fast automatic inner-ear segmentations.Raabid HussainAlain LalandeKibrom Berihu GirumCaroline GuigouAlexis Bozorg GrayeliNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Raabid Hussain Alain Lalande Kibrom Berihu Girum Caroline Guigou Alexis Bozorg Grayeli Automatic segmentation of inner ear on CT-scan using auto-context convolutional neural network
description	Abstract Temporal bone CT-scan is a prerequisite in most surgical procedures concerning the ear such as cochlear implants. The 3D vision of inner ear structures is crucial for diagnostic and surgical preplanning purposes. Since clinical CT-scans are acquired at relatively low resolutions, improved performance can be achieved by registering patient-specific CT images to a high-resolution inner ear model built from accurate 3D segmentations based on micro-CT of human temporal bone specimens. This paper presents a framework based on convolutional neural network for human inner ear segmentation from micro-CT images which can be used to build such a model from an extensive database. The proposed approach employs an auto-context based cascaded 2D U-net architecture with 3D connected component refinement to segment the cochlear scalae, semicircular canals, and the vestibule. The system was formulated on a data set composed of 17 micro-CT from public Hear-EU dataset. A Dice coefficient of 0.90 and Hausdorff distance of 0.74 mm were obtained. The system yielded precise and fast automatic inner-ear segmentations.
format	article
author	Raabid Hussain Alain Lalande Kibrom Berihu Girum Caroline Guigou Alexis Bozorg Grayeli
author_facet	Raabid Hussain Alain Lalande Kibrom Berihu Girum Caroline Guigou Alexis Bozorg Grayeli
author_sort	Raabid Hussain
title	Automatic segmentation of inner ear on CT-scan using auto-context convolutional neural network
title_short	Automatic segmentation of inner ear on CT-scan using auto-context convolutional neural network
title_full	Automatic segmentation of inner ear on CT-scan using auto-context convolutional neural network
title_fullStr	Automatic segmentation of inner ear on CT-scan using auto-context convolutional neural network
title_full_unstemmed	Automatic segmentation of inner ear on CT-scan using auto-context convolutional neural network
title_sort	automatic segmentation of inner ear on ct-scan using auto-context convolutional neural network
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/b23d705da78b4a40b68db403c37fc58f
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_version_	1718384192837910528

Automatic segmentation of inner ear on CT-scan using auto-context convolutional neural network

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