A review and comparison of breast tumor cell nuclei segmentation performances using deep convolutional neural networks

Abstract Breast cancer is currently the second most common cause of cancer-related death in women. Presently, the clinical benchmark in cancer diagnosis is tissue biopsy examination. However, the manual process of histopathological analysis is laborious, time-consuming, and limited by the quality of...

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Autores principales:	Andrew Lagree, Majidreza Mohebpour, Nicholas Meti, Khadijeh Saednia, Fang-I. Lu, Elzbieta Slodkowska, Sonal Gandhi, Eileen Rakovitch, Alex Shenfield, Ali Sadeghi-Naini, William T. Tran
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/d7bbeb5b75be4d00a2d11586bba4830e
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spelling	oai:doaj.org-article:d7bbeb5b75be4d00a2d11586bba4830e2021-12-02T15:51:13ZA review and comparison of breast tumor cell nuclei segmentation performances using deep convolutional neural networks10.1038/s41598-021-87496-12045-2322https://doaj.org/article/d7bbeb5b75be4d00a2d11586bba4830e2021-04-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-87496-1https://doaj.org/toc/2045-2322Abstract Breast cancer is currently the second most common cause of cancer-related death in women. Presently, the clinical benchmark in cancer diagnosis is tissue biopsy examination. However, the manual process of histopathological analysis is laborious, time-consuming, and limited by the quality of the specimen and the experience of the pathologist. This study's objective was to determine if deep convolutional neural networks can be trained, with transfer learning, on a set of histopathological images independent of breast tissue to segment tumor nuclei of the breast. Various deep convolutional neural networks were evaluated for the study, including U-Net, Mask R-CNN, and a novel network (GB U-Net). The networks were trained on a set of Hematoxylin and Eosin (H&E)-stained images of eight diverse types of tissues. GB U-Net demonstrated superior performance in segmenting sites of invasive diseases (AJI = 0.53, mAP = 0.39 & AJI = 0.54, mAP = 0.38), validated on two hold-out datasets exclusively containing breast tissue images of approximately 7,582 annotated cells. The results of the networks, trained on images independent of breast tissue, demonstrated that tumor nuclei of the breast could be accurately segmented.Andrew LagreeMajidreza MohebpourNicholas MetiKhadijeh SaedniaFang-I. LuElzbieta SlodkowskaSonal GandhiEileen RakovitchAlex ShenfieldAli Sadeghi-NainiWilliam T. TranNature 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 Andrew Lagree Majidreza Mohebpour Nicholas Meti Khadijeh Saednia Fang-I. Lu Elzbieta Slodkowska Sonal Gandhi Eileen Rakovitch Alex Shenfield Ali Sadeghi-Naini William T. Tran A review and comparison of breast tumor cell nuclei segmentation performances using deep convolutional neural networks
description	Abstract Breast cancer is currently the second most common cause of cancer-related death in women. Presently, the clinical benchmark in cancer diagnosis is tissue biopsy examination. However, the manual process of histopathological analysis is laborious, time-consuming, and limited by the quality of the specimen and the experience of the pathologist. This study's objective was to determine if deep convolutional neural networks can be trained, with transfer learning, on a set of histopathological images independent of breast tissue to segment tumor nuclei of the breast. Various deep convolutional neural networks were evaluated for the study, including U-Net, Mask R-CNN, and a novel network (GB U-Net). The networks were trained on a set of Hematoxylin and Eosin (H&E)-stained images of eight diverse types of tissues. GB U-Net demonstrated superior performance in segmenting sites of invasive diseases (AJI = 0.53, mAP = 0.39 & AJI = 0.54, mAP = 0.38), validated on two hold-out datasets exclusively containing breast tissue images of approximately 7,582 annotated cells. The results of the networks, trained on images independent of breast tissue, demonstrated that tumor nuclei of the breast could be accurately segmented.
format	article
author	Andrew Lagree Majidreza Mohebpour Nicholas Meti Khadijeh Saednia Fang-I. Lu Elzbieta Slodkowska Sonal Gandhi Eileen Rakovitch Alex Shenfield Ali Sadeghi-Naini William T. Tran
author_facet	Andrew Lagree Majidreza Mohebpour Nicholas Meti Khadijeh Saednia Fang-I. Lu Elzbieta Slodkowska Sonal Gandhi Eileen Rakovitch Alex Shenfield Ali Sadeghi-Naini William T. Tran
author_sort	Andrew Lagree
title	A review and comparison of breast tumor cell nuclei segmentation performances using deep convolutional neural networks
title_short	A review and comparison of breast tumor cell nuclei segmentation performances using deep convolutional neural networks
title_full	A review and comparison of breast tumor cell nuclei segmentation performances using deep convolutional neural networks
title_fullStr	A review and comparison of breast tumor cell nuclei segmentation performances using deep convolutional neural networks
title_full_unstemmed	A review and comparison of breast tumor cell nuclei segmentation performances using deep convolutional neural networks
title_sort	review and comparison of breast tumor cell nuclei segmentation performances using deep convolutional neural networks
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/d7bbeb5b75be4d00a2d11586bba4830e
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A review and comparison of breast tumor cell nuclei segmentation performances using deep convolutional neural networks

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