カメダ ヒロユキ   Hiroyuki KAMEDA
  亀田 弘之
   所属   コンピュータサイエンス学部 コンピュータサイエンス学科
   職種   教授
言語種別 英語
発行・発表の年月 2020/06
形態種別 学術論文
査読 査読あり
標題 Deep Learning of Cancer Stem Cell Morphology Using Conditional Generative Adversarial Networks
執筆形態 共著
掲載誌名 Biomolecules
掲載区分国外
出版社・発行元 MDPI
巻・号・頁 10(6)
総ページ数 13
著者・共著者 Saori Aida, Junpei Okugawa, Serena Fujisaka, Tomonari Kasai, Hiroyuki Kameda and Tomoyasu Sugiyama
概要 Deep-learning workflows of microscopic image analysis are sufficient for handling the contextual variations because they employ biological samples and have numerous tasks. The use of well-defined annotated images is important for the workflow. Cancer stem cells (CSCs) are identified by specific cell markers. These CSCs were extensively characterized by the stem cell (SC)-like gene expression and proliferation mechanisms for the development of tumors. In contrast, the morphological characterization remains elusive. This study aims to investigate the segmentation of CSCs in phase contrast imaging using conditional generative adversarial networks (CGAN). Artificial intelligence (AI) was trained using fluorescence images of the Nanog-Green fluorescence protein, the expression of which was maintained in CSCs, and the phase contrast images. The AI model segmented the CSC region in the phase contrast image of the CSC cultures and tumor model. By selecting images for training, several values for measuring segmentation quality increased. Moreover, nucleus fluorescence overlaid-phase contrast was effective for increasing the values. We show the possibility of mapping CSC morphology to the condition of undifferentiation using deep-learning CGAN workflows.
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