Author:
Lagree Andrew,Mohebpour Majidreza,Meti Nicholas,Saednia Khadijeh,Lu Fang-I.,Slodkowska Elzbieta,Gandhi Sonal,Rakovitch Eileen,Shenfield Alex,Sadeghi-Naini Ali,Tran William T.
Abstract
AbstractBreast 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.
Funder
Tri-Council (CIHR) Government of Canada’s New Frontiers in Research Fund
Natural Sciences and Engineering Research Council of Canada
Terry Fox Research Institute
Women’s Golf Health Classic Foundation Fund
Publisher
Springer Science and Business Media LLC
Cited by
51 articles.
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