EpithNet: Deep Regression for Epithelium Segmentation in Cervical Histology Images

Abstract

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.

Department(s)

Electrical and Computer Engineering

Second Department

Chemistry

Comments

Lister Hill National Center for Biomedical Communications, Grant None

Keywords and Phrases

Cervical Cancer; Cervical Intraepithelial Neoplasia; Convolutional Neural Network; Deep Learning; Image Processing; Segmentation

International Standard Serial Number (ISSN)

2229-5089; 2153-3539

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2020 Journal of Pathology Informatics, All rights reserved.

Publication Date

30 Mar 2020

Link to Full Text

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