Multi-level dilated residual network for biomedical image segmentation

Abstract We propose a novel multi-level dilated residual neural network, an extension of the classical U-Net architecture, for biomedical image segmentation. U-Net is the most popular deep neural architecture for biomedical image segmentation, however, despite being state-of-the-art, the model has a...

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Autores principales:	Naga Raju Gudhe, Hamid Behravan, Mazen Sudah, Hidemi Okuma, Ritva Vanninen, Veli-Matti Kosma, Arto Mannermaa
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/85e9a86e1d424523bea41de106b48ead
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spelling	oai:doaj.org-article:85e9a86e1d424523bea41de106b48ead2021-12-02T15:23:17ZMulti-level dilated residual network for biomedical image segmentation10.1038/s41598-021-93169-w2045-2322https://doaj.org/article/85e9a86e1d424523bea41de106b48ead2021-07-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-93169-whttps://doaj.org/toc/2045-2322Abstract We propose a novel multi-level dilated residual neural network, an extension of the classical U-Net architecture, for biomedical image segmentation. U-Net is the most popular deep neural architecture for biomedical image segmentation, however, despite being state-of-the-art, the model has a few limitations. In this study, we suggest replacing convolutional blocks of the classical U-Net with multi-level dilated residual blocks, resulting in enhanced learning capability. We also propose to incorporate a non-linear multi-level residual blocks into skip connections to reduce the semantic gap and to restore the information lost when concatenating features from encoder to decoder units. We evaluate the proposed approach on five publicly available biomedical datasets with different imaging modalities, including electron microscopy, magnetic resonance imaging, histopathology, and dermoscopy, each with its own segmentation challenges. The proposed approach consistently outperforms the classical U-Net by 2%, 3%, 6%, 8%, and 14% relative improvements in dice coefficient, respectively for magnetic resonance imaging, dermoscopy, histopathology, cell nuclei microscopy, and electron microscopy modalities. The visual assessments of the segmentation results further show that the proposed approach is robust against outliers and preserves better continuity in boundaries compared to the classical U-Net and its variant, MultiResUNet.Naga Raju GudheHamid BehravanMazen SudahHidemi OkumaRitva VanninenVeli-Matti KosmaArto MannermaaNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-18 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Naga Raju Gudhe Hamid Behravan Mazen Sudah Hidemi Okuma Ritva Vanninen Veli-Matti Kosma Arto Mannermaa Multi-level dilated residual network for biomedical image segmentation
description	Abstract We propose a novel multi-level dilated residual neural network, an extension of the classical U-Net architecture, for biomedical image segmentation. U-Net is the most popular deep neural architecture for biomedical image segmentation, however, despite being state-of-the-art, the model has a few limitations. In this study, we suggest replacing convolutional blocks of the classical U-Net with multi-level dilated residual blocks, resulting in enhanced learning capability. We also propose to incorporate a non-linear multi-level residual blocks into skip connections to reduce the semantic gap and to restore the information lost when concatenating features from encoder to decoder units. We evaluate the proposed approach on five publicly available biomedical datasets with different imaging modalities, including electron microscopy, magnetic resonance imaging, histopathology, and dermoscopy, each with its own segmentation challenges. The proposed approach consistently outperforms the classical U-Net by 2%, 3%, 6%, 8%, and 14% relative improvements in dice coefficient, respectively for magnetic resonance imaging, dermoscopy, histopathology, cell nuclei microscopy, and electron microscopy modalities. The visual assessments of the segmentation results further show that the proposed approach is robust against outliers and preserves better continuity in boundaries compared to the classical U-Net and its variant, MultiResUNet.
format	article
author	Naga Raju Gudhe Hamid Behravan Mazen Sudah Hidemi Okuma Ritva Vanninen Veli-Matti Kosma Arto Mannermaa
author_facet	Naga Raju Gudhe Hamid Behravan Mazen Sudah Hidemi Okuma Ritva Vanninen Veli-Matti Kosma Arto Mannermaa
author_sort	Naga Raju Gudhe
title	Multi-level dilated residual network for biomedical image segmentation
title_short	Multi-level dilated residual network for biomedical image segmentation
title_full	Multi-level dilated residual network for biomedical image segmentation
title_fullStr	Multi-level dilated residual network for biomedical image segmentation
title_full_unstemmed	Multi-level dilated residual network for biomedical image segmentation
title_sort	multi-level dilated residual network for biomedical image segmentation
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/85e9a86e1d424523bea41de106b48ead
work_keys_str_mv	AT nagarajugudhe multileveldilatedresidualnetworkforbiomedicalimagesegmentation AT hamidbehravan multileveldilatedresidualnetworkforbiomedicalimagesegmentation AT mazensudah multileveldilatedresidualnetworkforbiomedicalimagesegmentation AT hidemiokuma multileveldilatedresidualnetworkforbiomedicalimagesegmentation AT ritvavanninen multileveldilatedresidualnetworkforbiomedicalimagesegmentation AT velimattikosma multileveldilatedresidualnetworkforbiomedicalimagesegmentation AT artomannermaa multileveldilatedresidualnetworkforbiomedicalimagesegmentation
_version_	1718387274773692416

Multi-level dilated residual network for biomedical image segmentation

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