A deep learning based framework for the registration of three dimensional multi-modal medical images of the head

Abstract Image registration is a fundamental task in image analysis in which the transform that moves the coordinate system of one image to another is calculated. Registration of multi-modal medical images has important implications for clinical diagnosis, treatment planning, and image-guided surger...

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Autores principales:	Kh Tohidul Islam, Sudanthi Wijewickrema, Stephen O’Leary
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/01d9fbb0fb274af2aa4401bb64098747
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spelling	oai:doaj.org-article:01d9fbb0fb274af2aa4401bb640987472021-12-02T13:57:13ZA deep learning based framework for the registration of three dimensional multi-modal medical images of the head10.1038/s41598-021-81044-72045-2322https://doaj.org/article/01d9fbb0fb274af2aa4401bb640987472021-01-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-81044-7https://doaj.org/toc/2045-2322Abstract Image registration is a fundamental task in image analysis in which the transform that moves the coordinate system of one image to another is calculated. Registration of multi-modal medical images has important implications for clinical diagnosis, treatment planning, and image-guided surgery as it provides the means of bringing together complimentary information obtained from different image modalities. However, since different image modalities have different properties due to their different acquisition methods, it remains a challenging task to find a fast and accurate match between multi-modal images. Furthermore, due to reasons such as ethical issues and need for human expert intervention, it is difficult to collect a large database of labelled multi-modal medical images. In addition, manual input is required to determine the fixed and moving images as input to registration algorithms. In this paper, we address these issues and introduce a registration framework that (1) creates synthetic data to augment existing datasets, (2) generates ground truth data to be used in the training and testing of algorithms, (3) registers (using a combination of deep learning and conventional machine learning methods) multi-modal images in an accurate and fast manner, and (4) automatically classifies the image modality so that the process of registration can be fully automated. We validate the performance of the proposed framework on CT and MRI images of the head obtained from a publicly available registration database.Kh Tohidul IslamSudanthi WijewickremaStephen O’LearyNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-13 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Kh Tohidul Islam Sudanthi Wijewickrema Stephen O’Leary A deep learning based framework for the registration of three dimensional multi-modal medical images of the head
description	Abstract Image registration is a fundamental task in image analysis in which the transform that moves the coordinate system of one image to another is calculated. Registration of multi-modal medical images has important implications for clinical diagnosis, treatment planning, and image-guided surgery as it provides the means of bringing together complimentary information obtained from different image modalities. However, since different image modalities have different properties due to their different acquisition methods, it remains a challenging task to find a fast and accurate match between multi-modal images. Furthermore, due to reasons such as ethical issues and need for human expert intervention, it is difficult to collect a large database of labelled multi-modal medical images. In addition, manual input is required to determine the fixed and moving images as input to registration algorithms. In this paper, we address these issues and introduce a registration framework that (1) creates synthetic data to augment existing datasets, (2) generates ground truth data to be used in the training and testing of algorithms, (3) registers (using a combination of deep learning and conventional machine learning methods) multi-modal images in an accurate and fast manner, and (4) automatically classifies the image modality so that the process of registration can be fully automated. We validate the performance of the proposed framework on CT and MRI images of the head obtained from a publicly available registration database.
format	article
author	Kh Tohidul Islam Sudanthi Wijewickrema Stephen O’Leary
author_facet	Kh Tohidul Islam Sudanthi Wijewickrema Stephen O’Leary
author_sort	Kh Tohidul Islam
title	A deep learning based framework for the registration of three dimensional multi-modal medical images of the head
title_short	A deep learning based framework for the registration of three dimensional multi-modal medical images of the head
title_full	A deep learning based framework for the registration of three dimensional multi-modal medical images of the head
title_fullStr	A deep learning based framework for the registration of three dimensional multi-modal medical images of the head
title_full_unstemmed	A deep learning based framework for the registration of three dimensional multi-modal medical images of the head
title_sort	deep learning based framework for the registration of three dimensional multi-modal medical images of the head
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/01d9fbb0fb274af2aa4401bb64098747
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A deep learning based framework for the registration of three dimensional multi-modal medical images of the head

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