EpithNet: Deep regression for epithelium segmentation in cervical histology images

Journal of Pathology Informatics

关键词: cervical cancer;    cervical intraepithelial neoplasia;    convolutional neural network;    deep learning;    image processing;    segmentation;   

DOI  :  10.4103/jpi.jpi_53_19

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown