| Project/Area Number |
16K00394
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Research Field |
Life / Health / Medical informatics
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| Research Institution | Hiroshima University |
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Principal Investigator |
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| Project Period (FY) |
2016-04-01 – 2019-03-31
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| Project Status |
Completed (Fiscal Year 2018)
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| Budget Amount *help |
¥4,160,000 (Direct Cost: ¥3,200,000、Indirect Cost: ¥960,000)
Fiscal Year 2018: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
Fiscal Year 2017: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Fiscal Year 2016: ¥1,950,000 (Direct Cost: ¥1,500,000、Indirect Cost: ¥450,000)
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| Keywords | iPS 細胞 / 機械学習 / ニューラルネットワーク / 深層学習 / セグメンテーション / 異常検出 / iPS cells / Machine learning / Neural networks / deep learning / segmentation / CNN / colony detection / iPS cell colonies / 画像認識 |
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| Outline of Final Research Achievements |
In order to fulfill their promise in regenerative medicine and drug discovery, a steady supply of iPS cells obtained through harvesting of individual cell colonies is needed. However, cultivating iPS cell colonies is a sensitive process, and even if care is taken abnormalities can appear, which need to be detected. It is therefore important to automate the process of detecting such abnormalities and in this research project we have designed a new deep learning based semantic segmentation algorithm which is able to automatically detect and label at pixel level differentiated vs. undifferentiated cells in iPS colonies images.
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| Academic Significance and Societal Importance of the Research Achievements |
再生医療の実現に向けて需要が高まっているiPS細胞の大量生産を可能にするため，培養を自動化する過程で必要になるiPS細胞の分化・未分化の自動検出ができる新しいアルゴリズムを開発して，その有用性を検証した．また，本研究で開発したSemantic segmentation アルゴリズムが従来手法より高精度であるため，他の医用画像解析への応用も期待できる．
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