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Proceedings Paper

Cell nuclei segmentation in divergent images using deep learning and stochastic processing
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Paper Abstract

Traditional cell nucleus detection relies on pathologists with microscopes, which is a tedious, costly and time consuming progress. We develop a deep learning and stochastic processing method to auto-segment those microscopy images, named as Quick-in-process(Qip)-Net. Qip-Net was proposed as an automated method to detect cell nucleus under various conditions, such as randomized cell types, different magnifications, and varying image backgrounds. The network is constructed based on regions with convolution neural network features (RCNN). It is trained by 663 original images and their corresponding masks from Kaggle website. The results showed that Qip-Net could rapidly segment the cell nuclei from the testing dataset of complex and disruptive surroundings with better S-2 score around 3% compared to U-Net.

Paper Details

Date Published: 18 March 2019
PDF: 8 pages
Proc. SPIE 10956, Medical Imaging 2019: Digital Pathology, 1095616 (18 March 2019); doi: 10.1117/12.2513105
Show Author Affiliations
Qing Wu, Cleveland State Univ. (United States)
Shiyu Xu, Reflexion Medical (United States)
Hui Zhang, Cleveland State Univ. (United States)
Xuyang Shi, Cleveland State Univ. (United States)

Published in SPIE Proceedings Vol. 10956:
Medical Imaging 2019: Digital Pathology
John E. Tomaszewski; Aaron D. Ward, Editor(s)

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