Automatic microscopic cell counting by use of unsupervised adversarial domain adaptation and supervised density regression

Shenghua He; Kyaw Thu Minn; Lilianna Solnica-Krezel; Hua Li; Mark Anastasio

doi:10.1117/12.2513058

18 March 2019 Automatic microscopic cell counting by use of unsupervised adversarial domain adaptation and supervised density regression

Shenghua He, Kyaw Thu Minn, Lilianna Solnica-Krezel, Hua Li, Mark Anastasio

Author Affiliations +

Proceedings Volume 10956, Medical Imaging 2019: Digital Pathology; 1095604 (2019) https://doi.org/10.1117/12.2513058
Event: SPIE Medical Imaging, 2019, San Diego, California, United States

Abstract

Accurate cell counting in microscopic images is important for medical diagnoses and biological studies. However, manual cell counting is very time-consuming, tedious, and prone to subjective errors. We propose a new density regression-based method for automatic cell counting that reduces the need to manually annotate experimental images. A supervised learning-based density regression model (DRM) is trained with annotated synthetic images (the source domain) and their corresponding ground truth density maps. A domain adaptation model (DAM) is built to map experimental images (the target domain) to the feature space of the source domain. By use of the unsupervised learning-based DAM and supervised learning-based DRM, a cell density map of a given target image can be estimated, from which the number of cells can be counted. Results from experimental immunofluorescent microscopic images of human embryonic stem cells demonstrate the promising performance of the proposed counting method.

Conference Presentation

Citation Download Citation

Shenghua He, Kyaw Thu Minn, Lilianna Solnica-Krezel, Hua Li, and Mark Anastasio "Automatic microscopic cell counting by use of unsupervised adversarial domain adaptation and supervised density regression", Proc. SPIE 10956, Medical Imaging 2019: Digital Pathology, 1095604 (18 March 2019); https://doi.org/10.1117/12.2513058

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