| Project/Area Number |
18K06272
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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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| Review Section |
Basic Section 44020:Developmental biology-related
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| Research Institution | Center for Novel Science Initatives, National Institutes of Natural Sciences |
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Principal Investigator |
Kato Kagayaki 大学共同利用機関法人自然科学研究機構(新分野創成センター、アストロバイオロジーセンター、生命創成探究, 生命創成探究センター, 特任助教 (30391915)
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| Project Period (FY) |
2018-04-01 – 2022-03-31
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| Project Status |
Completed (Fiscal Year 2021)
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| Budget Amount *help |
¥4,420,000 (Direct Cost: ¥3,400,000、Indirect Cost: ¥1,020,000)
Fiscal Year 2020: ¥780,000 (Direct Cost: ¥600,000、Indirect Cost: ¥180,000)
Fiscal Year 2019: ¥260,000 (Direct Cost: ¥200,000、Indirect Cost: ¥60,000)
Fiscal Year 2018: ¥3,380,000 (Direct Cost: ¥2,600,000、Indirect Cost: ¥780,000)
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| Keywords | 生物画像データ解析 / 生物画像解析 / 生物画像情報解析 |
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| Outline of Final Research Achievements |
There is an urgent need to efficiently extract and measure biologically important features from observational images in multidimensional imaging and large-capacity biological research in recent years. Moreover, various image data are produced in the whole biological field due to the wide range of observation objects. To deal with this situation, we have developed software specializing in biological image data analysis. This software system is a foundation that enables simple expression and description of biological features in images as geometric expressions, and an application can be constructed by relatively small-scale programming. Using this system, we performed quantitative image analysis in a wide range of biological experiments, from intracellular organelles in cultured cell lines to analysis of free migration patterns in fish.
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| Academic Significance and Societal Importance of the Research Achievements |
本研究は，生物学研究において産生される画像データについて，その観察技法の発展に伴う多次元化・高精細化と近年のデータ科学的方法論に基づく大量データの解析法の開発という要望に対応するべく，生物画像データ解析に特化したソフトウェア基盤であり，特殊な顕微鏡からビデオ撮影した動画に至るまでの多岐に渡る画像データを透過的に処理，定量的な解析までを行うことを可能とするものである．今後，本システムを適用することによって，広範な生物学実験における観察画像の定量的な解析を効率よく行う系を構築することが可能となる．
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