Journal of Pathology Informatics Journal of Pathology Informatics
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ORIGINAL ARTICLE
Year : 2020  |  Volume : 11  |  Issue : 1  |  Page : 10

EpithNet: Deep regression for epithelium segmentation in cervical histology images


1 Department of Electrical and Computer Engineering, Missouri University of Science and Technology, Rolla, MO, USA
2 Department of Electrical and Computer Engineering, Missouri University of Science and Technology; Stoecker and Associates, Rolla, MO, USA
3 Lister Hill National Center for Biomedical Communications, National Library of Medicine, National Institutes of Health, DHHS, Bethesda, MD, USA
4 Department of Pathology, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
5 Department of Surgical Pathology, University of Missouri Hospitals and Clinics, Columbia, MO, USA

Correspondence Address:
Dr. R Joe Stanley
Department of Electrical and Computer Engineering, Missouri University of Science and Technology, 127 Emerson Electric Co. Hall, 301 W. 16th Street, Rolla, MO 65409-0040
USA
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/jpi.jpi_53_19

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Background: Automated pathology techniques for detecting cervical cancer at the premalignant stage have advantages for women in areas with limited medical resources. Methods: This article presents EpithNet, a deep learning approach for the critical step of automated epithelium segmentation in digitized cervical histology images. EpithNet employs three regression networks of varying dimensions of image input blocks (patches) surrounding a given pixel, with all blocks at a fixed resolution, using varying network depth. Results: The proposed model was evaluated on 311 digitized histology epithelial images and the results indicate that the technique maximizes region-based information to improve pixel-wise probability estimates. EpithNet-mc model, formed by intermediate concatenation of the convolutional layers of the three models, was observed to achieve 94% Jaccard index (intersection over union) which is 26.4% higher than the benchmark model. Conclusions: EpithNet yields better epithelial segmentation results than state-of-the-art benchmark methods.


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