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Proceedings Paper

Automatic microscopic cell counting by use of deeply-supervised density regression model
Author(s): Shenghua He; Kyaw Thu Minn; Lilianna Solnica-Krezel; Mark Anastasio; Hua Li
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Paper Abstract

Accurately counting cells in microscopic images is important for medical diagnoses and biological studies, but manual cell counting is very tedious, time-consuming, and prone to subjective errors, and automatic counting can be less accurate than desired. To improve the accuracy of automatic cell counting, we propose here a novel method that employs deeply-supervised density regression. A fully convolutional neural network (FCNN) serves as the primary FCNN for density map regression. Innovatively, a set of auxiliary FCNNs are employed to provide additional supervision for learning the intermediate layers of the primary CNN to improve network performance. In addition, the primary CNN is designed as a concatenating framework to integrate multi-scale features through shortcut connections in the network, which improves the granularity of the features extracted from the intermediate CNN layers and further supports the final density map estimation. The experimental results on immunofluorescent images of human embryonic stem cells demonstrate the superior performance of the proposed method over other state-of-the-art methods.

Paper Details

Date Published: 18 March 2019
PDF: 8 pages
Proc. SPIE 10956, Medical Imaging 2019: Digital Pathology, 109560L (18 March 2019); doi: 10.1117/12.2513045
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)
Mark Anastasio, Washington Univ. in St. Louis (United States)
Hua Li, 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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