Automatic Acquisition of Immune Cells Location Using Deep Learning for Automated Analysis

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  • 免疫細胞の自動解析に向けた深層学習による解析対象の自動指定

Abstract

<p>The demand for applying AI has increased to automate immune cell activity analysis in recent years. One of the reasons is the shortage of analysts. Also, the work requires a lot of time and effort because the immune cell’s activity is confirmed and analyzed while advancing the video frame by frame. Therefore, if cell images can automatically select using an AI classifier, the work time becomes short. In this research, we aim to shorten these tasks. For the AI classifier, this research used CNN, which is a kind of deep learning methods and conducted a study using cell images in advance. From the results of the study, it was confirmed by experiments that CNN exhibited good recognition performance. Next, we defined a “Recognition Frequency Space” for calculating the cumulative frequency of highly recognized regions and automatically selecting immune cells. By this method, we were able to generate detailed location information that is recognized as a cell. Using these, we were able to accurately cut out 10 immune cells from the frame image of the actual moving image. In this report, we show that multiple immune cells could be cut out automatically.</p>

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