Deep Learning of Cancer Stem Cell Morphology Using Conditional Generative Adversarial Networks
1
School of Computer Science, Tokyo University of Technology, 1401-1 Katakura-machi, Hachioji-shi, Tokyo 192-0982, Japan
2
Graduate School of Sciences and Technology for Innovation, Yamaguchi University, 2-16-1 Tokiwadai, Ube-shi, Yamaguchi 755-8611, Japan
3
School of Bioscience and Technology, Tokyo University of Technology, 1401-1 Katakura-machi, Hachioji-shi, Tokyo 192-0982, Japan
4
Neutron Therapy Research Center, Okayama University, 2-5-1 Shikada-cho, Kita-ku, Okayama 700-8558, Japan
*
Author to whom correspondence should be addressed.
†
Equal contribution.
Biomolecules 2020, 10(6), 931; https://doi.org/10.3390/biom10060931
Received: 22 May 2020
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Revised: 13 June 2020
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Accepted: 15 June 2020
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Published: 19 June 2020
(This article belongs to the Special Issue Application of Artificial Intelligence for Medical Research)
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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Keywords:
Cancer stem cell; conditional generative adversarial network; phase contrast; green fluorescence protein; tumor
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MDPI and ACS Style
Aida, S.; Okugawa, J.; Fujisaka, S.; Kasai, T.; Kameda, H.; Sugiyama, T. Deep Learning of Cancer Stem Cell Morphology Using Conditional Generative Adversarial Networks. Biomolecules 2020, 10, 931. https://doi.org/10.3390/biom10060931
AMA Style
Aida S, Okugawa J, Fujisaka S, Kasai T, Kameda H, Sugiyama T. Deep Learning of Cancer Stem Cell Morphology Using Conditional Generative Adversarial Networks. Biomolecules. 2020; 10(6):931. https://doi.org/10.3390/biom10060931
Chicago/Turabian StyleAida, Saori, Junpei Okugawa, Serena Fujisaka, Tomonari Kasai, Hiroyuki Kameda, and Tomoyasu Sugiyama. 2020. "Deep Learning of Cancer Stem Cell Morphology Using Conditional Generative Adversarial Networks" Biomolecules 10, no. 6: 931. https://doi.org/10.3390/biom10060931
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