2023 Fiscal Year Research-status Report
DeepAD: An automated and interpretable machine learning pipeline for image analyses of biomarkers in Alzheimer's disease
| Project/Area Number |
22K15658
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| Research Institution | Institute of Physical and Chemical Research |
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Principal Investigator |
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| Project Period (FY) |
2022-04-01 – 2026-03-31
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| Keywords | segmentation / alzheimer's disease / glial cells / plaques / deep learning |
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| Outline of Annual Research Achievements |
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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| Current Status of Research Progress |
Current Status of Research Progress
3: Progress in research has been slightly delayed.
Reason
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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| Strategy for Future Research Activity |
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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| Causes of Carryover |
Due to the focus on methodology development, there was an adjustment in the direct cost budget breakdown. The incurring amount, along with the budget of the next fiscal year, will be used to purchase equipment for data storage and collaborator use, and to encourage further collaboration by attending conferences.
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