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Proceedings Paper

Automatic microscopic cell counting by use of unsupervised adversarial domain adaptation and supervised density regression
Author(s): Shenghua He; Kyaw Thu Minn; Lilianna Solnica-Krezel; Hua Li; Mark Anastasio
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Paper 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.

Paper Details

Date Published: 18 March 2019
PDF: 8 pages
Proc. SPIE 10956, Medical Imaging 2019: Digital Pathology, 1095604 (18 March 2019); doi: 10.1117/12.2513058
Show Author Affiliations
Shenghua He, Washington Univ. in St. Louis (United States)
Kyaw Thu Minn, Washington Univ. in St. Louis (United States)
Lilianna Solnica-Krezel, Washington Univ. in St. Louis (United States)
Hua Li, Washington Univ. in St. Louis (United States)
Mark Anastasio, Washington Univ. in St. Louis (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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