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 digiti...
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| Main Authors: | , , , , , , , , |
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| Format: | Book |
| Published: |
Elsevier,
2020-01-01T00:00:00Z.
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| Online Access: | Connect to this object online. |
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| Summary: | 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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| Item Description: | 2153-3539 2153-3539 10.4103/jpi.jpi_53_19 |