| 研究課題/領域番号 |
22K15658
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| 研究種目 |
若手研究
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| 配分区分 | 基金 |
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| 審査区分 |
小区分51030:病態神経科学関連
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| 研究機関 | 国立研究開発法人理化学研究所 |
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研究代表者 |
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| 研究期間 (年度) |
2022-04-01 – 2026-03-31
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| 研究課題ステータス |
交付 (2023年度)
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| 配分額 *注記 |
4,420千円 (直接経費: 3,400千円、間接経費: 1,020千円)
2025年度: 910千円 (直接経費: 700千円、間接経費: 210千円)
2024年度: 1,040千円 (直接経費: 800千円、間接経費: 240千円)
2023年度: 910千円 (直接経費: 700千円、間接経費: 210千円)
2022年度: 1,560千円 (直接経費: 1,200千円、間接経費: 360千円)
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| キーワード | segmentation / alzheimer's disease / glial cells / plaques / deep learning / amyloid / image processing / microscopy / 画像情報処理・画像認識 / 分子・細胞・神経生物学 / 認知症疾患 / グリア細胞 / コンピュータビジョン |
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| 研究開始時の研究の概要 |
Our goal is to create an automated tool to conduct image analyses common in Alzheimer's disease research, specifically: quantification of pathological Aβ plaques and glial cells. Automating analyses will reduce human error and bias, thereby improving the reproducibility of Alzheimer's disease research. The tool will be developed in collaboration with neuroscientists to ensure that it is easily understood and useful.
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| 研究実績の概要 |
The purpose of the project is to develop an interpretable image processing pipeline that uses deep learning tools to automate image analyses of AD microscopy images. In FY2022 and FY2023, the Research Plan was to focus on image preprocessing and processing of Ab plaques in images.
In the previous year, it became evident that it was necessary to develop methods that can handle images of varying contrast, brightness, hue, etc. To this end, we developed a deep learning meta-network that learns to combine different segmentation maps to generate one that most closely resembles the ground truth label. This work will be published as a short paper in the Medical Imaging with Deep Learning conference this year. The meta-network will be helpful in segmenting AD microscopy datasets, which generally have low signal-to-noise ratio, particularly at advanced stages of disease.
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| 現在までの達成度 (区分) |
現在までの達成度 (区分)
3: やや遅れている
理由
Progress is being made in developing deep learning networks for segmentation, as well as automated processing pipelines and data organizing structures. These results have been published in international and local conferences. However, there is some delay in generating specific results for the datasets that we have, and in obtaining more diverse datasets. We aim to apply the tools that we have developed in the previous 2 years in the current fiscal year.
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| 今後の研究の推進方策 |
For the current fiscal year, we plan to apply the tools that we developed to the AD datasets and present these findings at the Japanese Neuroscience Conference. At the conference, we also plan to recruit imaging datasets from other researchers in the AD field, for the purpose of further developing our pipeline and to encourage collaboration.
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