Abstract

Background: Advances in image analysis and computational techniques have facilitated automatic detection of critical features in histopathology images. Detection of nuclei is critical for squamous epithelium cervical intraepithelial neoplasia (CIN) classification into normal, CIN1, CIN2, and CIN3 grades.

Methods: In this study, a deep learning (DL)-based nuclei segmentation approach is investigated based on gathering localized information through the generation of superpixels using a simple linear iterative clustering algorithm and training with a convolutional neural network.

Results: The proposed approach was evaluated on a dataset of 133 digitized histology images and achieved an overall nuclei detection (object-based) accuracy of 95.97%, with demonstrated improvement over imaging-based and clustering-based benchmark techniques.

Conclusions: The proposed DL-based nuclei segmentation Method with superpixel analysis has shown improved segmentation results in comparison to state-of-the-art methods.

Department(s)

Electrical and Computer Engineering

Second Department

Chemistry

Keywords and Phrases

Cervical cancer; Cervical intraepithelial neoplasia; Convolutional neural network; Deep learning; Image processing; Segmentation; Superpixels

International Standard Serial Number (ISSN)

2229-5089

Document Type

Article - Journal

Document Version

Final Version

File Type

text

Language(s)

English

Rights

© 2018 Medknow Publications, All rights reserved.

Creative Commons Licensing

Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 4.0 License.

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