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

LAGREE, Andrew, MOHEBPOUR, Majidreza, METI, Nicholas, SAEDNIA, Khadijeh, LU, Fang-I., SLODKOWSKA, Elzbieta, GANDHI, Sonal, RAKOVITCH, Eileen, SHENFIELD, Alex, SADEGHI-NAINI, Ali and TRAN, William (2021). A review and comparison of breast tumor cell nuclei segmentation performances using deep convolutional neural networks. Scientific Reports, 11 (1), p. 8025.

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Abstract

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.

Item Type:	Article
Additional Information:	** From Springer Nature via Jisc Publications Router ** Licence for this article: http://creativecommons.org/licenses/by/4.0/ **Journal IDs: eissn 2045-2322 **Article IDs: publisher-id: s41598-021-87496-1; manuscript: 87496 **History: collection 12-2021; published 13-04-2021; online 13-04-2021; registration 31-03-2021; accepted 30-03-2021; submitted 06-10-2020
Uncontrolled Keywords:	Article, /631/67/1347, /639/166/987, article
Identification Number:	https://doi.org/10.1038/s41598-021-87496-1
Page Range:	p. 8025
SWORD Depositor:	Colin Knott
Depositing User:	Colin Knott
Date Deposited:	16 Apr 2021 14:52
Last Modified:	16 Apr 2021 15:00
URI:	https://shura.shu.ac.uk/id/eprint/28507

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