Simultaneous lesion and brain segmentation in multiple sclerosis using deep neural networks

Abstract Segmentation of white matter lesions and deep grey matter structures is an important task in the quantification of magnetic resonance imaging in multiple sclerosis. In this paper we explore segmentation solutions based on convolutional neural networks (CNNs) for providing fast, reliable seg...

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Autores principales:	Richard McKinley, Rik Wepfer, Fabian Aschwanden, Lorenz Grunder, Raphaela Muri, Christian Rummel, Rajeev Verma, Christian Weisstanner, Mauricio Reyes, Anke Salmen, Andrew Chan, Franca Wagner, Roland Wiest
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/2d91ebaae8b1419a8505044be986094e
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spelling	oai:doaj.org-article:2d91ebaae8b1419a8505044be986094e2021-12-02T14:12:42ZSimultaneous lesion and brain segmentation in multiple sclerosis using deep neural networks10.1038/s41598-020-79925-42045-2322https://doaj.org/article/2d91ebaae8b1419a8505044be986094e2021-01-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-79925-4https://doaj.org/toc/2045-2322Abstract Segmentation of white matter lesions and deep grey matter structures is an important task in the quantification of magnetic resonance imaging in multiple sclerosis. In this paper we explore segmentation solutions based on convolutional neural networks (CNNs) for providing fast, reliable segmentations of lesions and grey-matter structures in multi-modal MR imaging, and the performance of these methods when applied to out-of-centre data. We trained two state-of-the-art fully convolutional CNN architectures on the 2016 MSSEG training dataset, which was annotated by seven independent human raters: a reference implementation of a 3D Unet, and a more recently proposed 3D-to-2D architecture (DeepSCAN). We then retrained those methods on a larger dataset from a single centre, with and without labels for other brain structures. We quantified changes in performance owing to dataset shift, and changes in performance by adding the additional brain-structure labels. We also compared performance with freely available reference methods. Both fully-convolutional CNN methods substantially outperform other approaches in the literature when trained and evaluated in cross-validation on the MSSEG dataset, showing agreement with human raters in the range of human inter-rater variability. Both architectures showed drops in performance when trained on single-centre data and tested on the MSSEG dataset. When trained with the addition of weak anatomical labels derived from Freesurfer, the performance of the 3D Unet degraded, while the performance of the DeepSCAN net improved. Overall, the DeepSCAN network predicting both lesion and anatomical labels was the best-performing network examined.Richard McKinleyRik WepferFabian AschwandenLorenz GrunderRaphaela MuriChristian RummelRajeev VermaChristian WeisstannerMauricio ReyesAnke SalmenAndrew ChanFranca WagnerRoland WiestNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Richard McKinley Rik Wepfer Fabian Aschwanden Lorenz Grunder Raphaela Muri Christian Rummel Rajeev Verma Christian Weisstanner Mauricio Reyes Anke Salmen Andrew Chan Franca Wagner Roland Wiest Simultaneous lesion and brain segmentation in multiple sclerosis using deep neural networks
description	Abstract Segmentation of white matter lesions and deep grey matter structures is an important task in the quantification of magnetic resonance imaging in multiple sclerosis. In this paper we explore segmentation solutions based on convolutional neural networks (CNNs) for providing fast, reliable segmentations of lesions and grey-matter structures in multi-modal MR imaging, and the performance of these methods when applied to out-of-centre data. We trained two state-of-the-art fully convolutional CNN architectures on the 2016 MSSEG training dataset, which was annotated by seven independent human raters: a reference implementation of a 3D Unet, and a more recently proposed 3D-to-2D architecture (DeepSCAN). We then retrained those methods on a larger dataset from a single centre, with and without labels for other brain structures. We quantified changes in performance owing to dataset shift, and changes in performance by adding the additional brain-structure labels. We also compared performance with freely available reference methods. Both fully-convolutional CNN methods substantially outperform other approaches in the literature when trained and evaluated in cross-validation on the MSSEG dataset, showing agreement with human raters in the range of human inter-rater variability. Both architectures showed drops in performance when trained on single-centre data and tested on the MSSEG dataset. When trained with the addition of weak anatomical labels derived from Freesurfer, the performance of the 3D Unet degraded, while the performance of the DeepSCAN net improved. Overall, the DeepSCAN network predicting both lesion and anatomical labels was the best-performing network examined.
format	article
author	Richard McKinley Rik Wepfer Fabian Aschwanden Lorenz Grunder Raphaela Muri Christian Rummel Rajeev Verma Christian Weisstanner Mauricio Reyes Anke Salmen Andrew Chan Franca Wagner Roland Wiest
author_facet	Richard McKinley Rik Wepfer Fabian Aschwanden Lorenz Grunder Raphaela Muri Christian Rummel Rajeev Verma Christian Weisstanner Mauricio Reyes Anke Salmen Andrew Chan Franca Wagner Roland Wiest
author_sort	Richard McKinley
title	Simultaneous lesion and brain segmentation in multiple sclerosis using deep neural networks
title_short	Simultaneous lesion and brain segmentation in multiple sclerosis using deep neural networks
title_full	Simultaneous lesion and brain segmentation in multiple sclerosis using deep neural networks
title_fullStr	Simultaneous lesion and brain segmentation in multiple sclerosis using deep neural networks
title_full_unstemmed	Simultaneous lesion and brain segmentation in multiple sclerosis using deep neural networks
title_sort	simultaneous lesion and brain segmentation in multiple sclerosis using deep neural networks
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/2d91ebaae8b1419a8505044be986094e
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Simultaneous lesion and brain segmentation in multiple sclerosis using deep neural networks

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