Automatic microscopic cell counting by use of deeply-supervised density regression model

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

doi:10.1117/12.2513045

18 March 2019 Automatic microscopic cell counting by use of deeply-supervised density regression model

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

Proceedings Volume 10956, Medical Imaging 2019: Digital Pathology; 109560L (2019) https://doi.org/10.1117/12.2513045
Event: SPIE Medical Imaging, 2019, San Diego, California, United States

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.

Conference Presentation

Citation Download Citation

Shenghua He, Kyaw Thu Minn, Lilianna Solnica-Krezel, Mark Anastasio, and Hua Li "Automatic microscopic cell counting by use of deeply-supervised density regression model", Proc. SPIE 10956, Medical Imaging 2019: Digital Pathology, 109560L (18 March 2019); https://doi.org/10.1117/12.2513045

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