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

Qing Wu; Shiyu Xu; Hui Zhang; Xuyang Shi

doi:10.1117/12.2513105

18 March 2019 Cell nuclei segmentation in divergent images using deep learning and stochastic processing

Qing Wu, Shiyu Xu, Hui Zhang, Xuyang Shi

Proceedings Volume 10956, Medical Imaging 2019: Digital Pathology; 1095616 (2019) https://doi.org/10.1117/12.2513105
Event: SPIE Medical Imaging, 2019, San Diego, California, United States

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.

Citation Download Citation

Qing Wu, Shiyu Xu, Hui Zhang, and Xuyang Shi "Cell nuclei segmentation in divergent images using deep learning and stochastic processing", Proc. SPIE 10956, Medical Imaging 2019: Digital Pathology, 1095616 (18 March 2019); https://doi.org/10.1117/12.2513105

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available