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KMID : 0892920230320030181
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Experimental Neurobiology
2023 Volume.32 No. 3 p.181 ~ p.194
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An Automated Cell Detection Method for TH-positive Dopaminergic Neurons in a Mouse Model of Parkinson’s Disease Using Convolutional Neural Networks
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Kim Do-Yun
Bak Myeong-Seong
Park Ha-Ney
Domenico Mascagni
Chung Gee-Hoon
Park Jae-Hyun
Ahn So-Ra
Park Seon-Young
Bae Hyun-Su
Park Hi-Joon
Kim Sun-Kwang
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Abstract
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Quantification of tyrosine hydroxylase (TH)-positive neurons is essential for the preclinical study of Parkinson’s disease (PD). However, manual analysis of immunohistochemical (IHC) images is labor-intensive and has less reproducibility due to the lack of objectivity. Therefore, several automated methods of IHC image analysis have been proposed, although they have limitations of low accuracy and difficulties in practical use. Here, we developed a convolutional neural network-based machine learning algorithm for TH+ cell counting. The developed analytical tool showed higher accuracy than the conventional methods and could be used under diverse experimental conditions of image staining intensity, brightness, and contrast. Our automated cell detection algorithm is available for free and has an intelligible graphical user interface for cell counting to assist practical applications. Overall, we expect that the proposed TH+ cell counting tool will promote preclinical PD research by saving time and enabling objective analysis of IHC images.
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KEYWORD
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Parkinson′s disease, Mice, Dopaminergic neurons, Deep learning, Neural networks, Cell count
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