EpithNet: Deep Regression for Epithelium Segmentation in Cervical Histology Images
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.
S. Sornapudi et al., "EpithNet: Deep Regression for Epithelium Segmentation in Cervical Histology Images," Journal of Pathology Informatics, vol. 11, no. 1, Journal of Pathology Informatics, Mar 2020.
The definitive version is available at https://doi.org/10.4103/jpi.jpi_53_19
Electrical and Computer Engineering
Keywords and Phrases
Cervical Cancer; Cervical Intraepithelial Neoplasia; Convolutional Neural Network; Deep Learning; Image Processing; Segmentation
International Standard Serial Number (ISSN)
Article - Journal
© 2020 Journal of Pathology Informatics, All rights reserved.
30 Mar 2020