Artificial intelligence pathological diagnosis by hyperspectral nonlinear Raman scattering imaging
| Project/Area Number |
17H02793
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| Research Category |
Grant-in-Aid for Scientific Research (B)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Optical engineering, Photon science
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| Research Institution | Hokkaido University |
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Principal Investigator |
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| Co-Investigator(Kenkyū-buntansha) |
高松 哲郎 京都府立医科大学, 医学(系)研究科(研究院), 教授 (40154900)
加藤 祐次 北海道大学, 情報科学研究院, 助教 (50261582)
三宅 淳 大阪大学, 国際医工情報センター, 特任教授 (70344174)
新岡 宏彦 大阪大学, データビリティフロンティア機構, 特任准教授(常勤) (70552074)
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| Project Period (FY) |
2017-04-01 – 2020-03-31
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| Project Status |
Completed (Fiscal Year 2019)
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| Budget Amount *help |
¥17,680,000 (Direct Cost: ¥13,600,000、Indirect Cost: ¥4,080,000)
Fiscal Year 2019: ¥3,640,000 (Direct Cost: ¥2,800,000、Indirect Cost: ¥840,000)
Fiscal Year 2018: ¥5,980,000 (Direct Cost: ¥4,600,000、Indirect Cost: ¥1,380,000)
Fiscal Year 2017: ¥8,060,000 (Direct Cost: ¥6,200,000、Indirect Cost: ¥1,860,000)
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| Keywords | 深層学習 / 非線形ラマン散乱 / ハイパースペクトルイメージング / 生体光計測 / ラマンイメージング / 非線形ラマン散乱イメージ |
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| Outline of Final Research Achievements |
By reducing the noise of observation data and improvement of observation time using deep learning, we succeeded in the imaging rate improvement of 1.6–1.2 image/min. to 12.5-4.0 image/min. In tissue classification by machine learning of nonlinear Raman scattering images, pre-training with fluorescence images significantly improved the segmentation ability. Besides, we have developed a new microscope for acquiring a large number of hyperspectral nonlinear Raman scattering images and succeeded in increasing the speed 14 times and reducing the excitation light peak irradiance 1/12 compared to the conventional method. It was shown that hyperspectral images of cultured cells were classified by deep learning, and that unsupervised learning could classify cells with different culture conditions.
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| Academic Significance and Societal Importance of the Research Achievements |
近年の病理医不足の解消には，一人の病理医が処理できる病理診断数の向上が課題であり，病理検査の効率化，自動化を行なうと共に，自動的に病理診断およびそのスクリーニングを行なうシステムの開発が重要である．従来にない新しいイメージング手法と人工知能の組み合わせにより，イメージング速度の向上や分別能力の向上が示された．医師の負担削減によりより多くの人々の健康な生活のサポートが可能となる．
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Report
(4 results)
Research Products
(31 results)
