Connected-UNets: a deep learning architecture for breast mass segmentation

Abstract Breast cancer analysis implies that radiologists inspect mammograms to detect suspicious breast lesions and identify mass tumors. Artificial intelligence techniques offer automatic systems for breast mass segmentation to assist radiologists in their diagnosis. With the rapid development of...

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Autores principales:	Asma Baccouche, Begonya Garcia-Zapirain, Cristian Castillo Olea, Adel S. Elmaghraby
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282
Acceso en línea:	https://doaj.org/article/dccc5b2e5f29459eabd921a1aed54115
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spelling	oai:doaj.org-article:dccc5b2e5f29459eabd921a1aed541152021-12-05T12:20:20ZConnected-UNets: a deep learning architecture for breast mass segmentation10.1038/s41523-021-00358-x2374-4677https://doaj.org/article/dccc5b2e5f29459eabd921a1aed541152021-12-01T00:00:00Zhttps://doi.org/10.1038/s41523-021-00358-xhttps://doaj.org/toc/2374-4677Abstract Breast cancer analysis implies that radiologists inspect mammograms to detect suspicious breast lesions and identify mass tumors. Artificial intelligence techniques offer automatic systems for breast mass segmentation to assist radiologists in their diagnosis. With the rapid development of deep learning and its application to medical imaging challenges, UNet and its variations is one of the state-of-the-art models for medical image segmentation that showed promising performance on mammography. In this paper, we propose an architecture, called Connected-UNets, which connects two UNets using additional modified skip connections. We integrate Atrous Spatial Pyramid Pooling (ASPP) in the two standard UNets to emphasize the contextual information within the encoder–decoder network architecture. We also apply the proposed architecture on the Attention UNet (AUNet) and the Residual UNet (ResUNet). We evaluated the proposed architectures on two publically available datasets, the Curated Breast Imaging Subset of Digital Database for Screening Mammography (CBIS-DDSM) and INbreast, and additionally on a private dataset. Experiments were also conducted using additional synthetic data using the cycle-consistent Generative Adversarial Network (CycleGAN) model between two unpaired datasets to augment and enhance the images. Qualitative and quantitative results show that the proposed architecture can achieve better automatic mass segmentation with a high Dice score of 89.52%, 95.28%, and 95.88% and Intersection over Union (IoU) score of 80.02%, 91.03%, and 92.27%, respectively, on CBIS-DDSM, INbreast, and the private dataset.Asma BaccoucheBegonya Garcia-ZapirainCristian Castillo OleaAdel S. ElmaghrabyNature PortfolioarticleNeoplasms. Tumors. Oncology. Including cancer and carcinogensRC254-282ENnpj Breast Cancer, Vol 7, Iss 1, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282
spellingShingle	Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282 Asma Baccouche Begonya Garcia-Zapirain Cristian Castillo Olea Adel S. Elmaghraby Connected-UNets: a deep learning architecture for breast mass segmentation
description	Abstract Breast cancer analysis implies that radiologists inspect mammograms to detect suspicious breast lesions and identify mass tumors. Artificial intelligence techniques offer automatic systems for breast mass segmentation to assist radiologists in their diagnosis. With the rapid development of deep learning and its application to medical imaging challenges, UNet and its variations is one of the state-of-the-art models for medical image segmentation that showed promising performance on mammography. In this paper, we propose an architecture, called Connected-UNets, which connects two UNets using additional modified skip connections. We integrate Atrous Spatial Pyramid Pooling (ASPP) in the two standard UNets to emphasize the contextual information within the encoder–decoder network architecture. We also apply the proposed architecture on the Attention UNet (AUNet) and the Residual UNet (ResUNet). We evaluated the proposed architectures on two publically available datasets, the Curated Breast Imaging Subset of Digital Database for Screening Mammography (CBIS-DDSM) and INbreast, and additionally on a private dataset. Experiments were also conducted using additional synthetic data using the cycle-consistent Generative Adversarial Network (CycleGAN) model between two unpaired datasets to augment and enhance the images. Qualitative and quantitative results show that the proposed architecture can achieve better automatic mass segmentation with a high Dice score of 89.52%, 95.28%, and 95.88% and Intersection over Union (IoU) score of 80.02%, 91.03%, and 92.27%, respectively, on CBIS-DDSM, INbreast, and the private dataset.
format	article
author	Asma Baccouche Begonya Garcia-Zapirain Cristian Castillo Olea Adel S. Elmaghraby
author_facet	Asma Baccouche Begonya Garcia-Zapirain Cristian Castillo Olea Adel S. Elmaghraby
author_sort	Asma Baccouche
title	Connected-UNets: a deep learning architecture for breast mass segmentation
title_short	Connected-UNets: a deep learning architecture for breast mass segmentation
title_full	Connected-UNets: a deep learning architecture for breast mass segmentation
title_fullStr	Connected-UNets: a deep learning architecture for breast mass segmentation
title_full_unstemmed	Connected-UNets: a deep learning architecture for breast mass segmentation
title_sort	connected-unets: a deep learning architecture for breast mass segmentation
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/dccc5b2e5f29459eabd921a1aed54115
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Connected-UNets: a deep learning architecture for breast mass segmentation

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