CMM-Net: Contextual multi-scale multi-level network for efficient biomedical image segmentation

Abstract Medical image segmentation of tissue abnormalities, key organs, or blood vascular system is of great significance for any computerized diagnostic system. However, automatic segmentation in medical image analysis is a challenging task since it requires sophisticated knowledge of the target o...

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Autores principales:	Mohammed A. Al-masni, Dong-Hyun Kim
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/2ecd304622bb4e2e98a20482db384238
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spelling	oai:doaj.org-article:2ecd304622bb4e2e98a20482db3842382021-12-02T16:50:32ZCMM-Net: Contextual multi-scale multi-level network for efficient biomedical image segmentation10.1038/s41598-021-89686-32045-2322https://doaj.org/article/2ecd304622bb4e2e98a20482db3842382021-05-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-89686-3https://doaj.org/toc/2045-2322Abstract Medical image segmentation of tissue abnormalities, key organs, or blood vascular system is of great significance for any computerized diagnostic system. However, automatic segmentation in medical image analysis is a challenging task since it requires sophisticated knowledge of the target organ anatomy. This paper develops an end-to-end deep learning segmentation method called Contextual Multi-Scale Multi-Level Network (CMM-Net). The main idea is to fuse the global contextual features of multiple spatial scales at every contracting convolutional network level in the U-Net. Also, we re-exploit the dilated convolution module that enables an expansion of the receptive field with different rates depending on the size of feature maps throughout the networks. In addition, an augmented testing scheme referred to as Inversion Recovery (IR) which uses logical “OR” and “AND” operators is developed. The proposed segmentation network is evaluated on three medical imaging datasets, namely ISIC 2017 for skin lesions segmentation from dermoscopy images, DRIVE for retinal blood vessels segmentation from fundus images, and BraTS 2018 for brain gliomas segmentation from MR scans. The experimental results showed superior state-of-the-art performance with overall dice similarity coefficients of 85.78%, 80.27%, and 88.96% on the segmentation of skin lesions, retinal blood vessels, and brain tumors, respectively. The proposed CMM-Net is inherently general and could be efficiently applied as a robust tool for various medical image segmentations.Mohammed A. Al-masniDong-Hyun KimNature 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 Mohammed A. Al-masni Dong-Hyun Kim CMM-Net: Contextual multi-scale multi-level network for efficient biomedical image segmentation
description	Abstract Medical image segmentation of tissue abnormalities, key organs, or blood vascular system is of great significance for any computerized diagnostic system. However, automatic segmentation in medical image analysis is a challenging task since it requires sophisticated knowledge of the target organ anatomy. This paper develops an end-to-end deep learning segmentation method called Contextual Multi-Scale Multi-Level Network (CMM-Net). The main idea is to fuse the global contextual features of multiple spatial scales at every contracting convolutional network level in the U-Net. Also, we re-exploit the dilated convolution module that enables an expansion of the receptive field with different rates depending on the size of feature maps throughout the networks. In addition, an augmented testing scheme referred to as Inversion Recovery (IR) which uses logical “OR” and “AND” operators is developed. The proposed segmentation network is evaluated on three medical imaging datasets, namely ISIC 2017 for skin lesions segmentation from dermoscopy images, DRIVE for retinal blood vessels segmentation from fundus images, and BraTS 2018 for brain gliomas segmentation from MR scans. The experimental results showed superior state-of-the-art performance with overall dice similarity coefficients of 85.78%, 80.27%, and 88.96% on the segmentation of skin lesions, retinal blood vessels, and brain tumors, respectively. The proposed CMM-Net is inherently general and could be efficiently applied as a robust tool for various medical image segmentations.
format	article
author	Mohammed A. Al-masni Dong-Hyun Kim
author_facet	Mohammed A. Al-masni Dong-Hyun Kim
author_sort	Mohammed A. Al-masni
title	CMM-Net: Contextual multi-scale multi-level network for efficient biomedical image segmentation
title_short	CMM-Net: Contextual multi-scale multi-level network for efficient biomedical image segmentation
title_full	CMM-Net: Contextual multi-scale multi-level network for efficient biomedical image segmentation
title_fullStr	CMM-Net: Contextual multi-scale multi-level network for efficient biomedical image segmentation
title_full_unstemmed	CMM-Net: Contextual multi-scale multi-level network for efficient biomedical image segmentation
title_sort	cmm-net: contextual multi-scale multi-level network for efficient biomedical image segmentation
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/2ecd304622bb4e2e98a20482db384238
work_keys_str_mv	AT mohammedaalmasni cmmnetcontextualmultiscalemultilevelnetworkforefficientbiomedicalimagesegmentation AT donghyunkim cmmnetcontextualmultiscalemultilevelnetworkforefficientbiomedicalimagesegmentation
_version_	1718383027990560768

CMM-Net: Contextual multi-scale multi-level network for efficient biomedical image segmentation

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