Kim Doyun, Bak Myeong Seong, Park Haney, Baek In Seon, Chung Geehoon, Park Jae Hyun, Ahn Sora, Park Seon-Young, Bae Hyunsu, Park Hi-Joon, Kim Sun Kwang
Department of Physiology, College of Korean Medicine, Kyung Hee University, Seoul 02447, Korea.
Department of Science in Korean Medicine, Graduate School, Kyung Hee University, Seoul 02447, Korea.
Exp Neurobiol. 2023 Jun 30;32(3):181-194. doi: 10.5607/en23001.
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.
酪氨酸羟化酶（TH）阳性神经元的定量分析对于帕金森病（PD）的临床前研究至关重要。然而，免疫组织化学（IHC）图像的手动分析劳动强度大，且由于缺乏客观性，重现性较差。因此，尽管几种IHC图像分析的自动化方法存在准确性低和实际应用困难等局限性，但仍被提出。在此，我们开发了一种基于卷积神经网络的机器学习算法用于TH +细胞计数。所开发的分析工具显示出比传统方法更高的准确性，并且可以在图像染色强度、亮度和对比度的各种实验条件下使用。我们的自动细胞检测算法免费可用，并且具有用于细胞计数的直观图形用户界面以辅助实际应用。总体而言，我们期望所提出的TH +细胞计数工具将通过节省时间和实现IHC图像的客观分析来促进临床前PD研究。
