| Project/Area Number |
23KF0296
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| Research Category |
Grant-in-Aid for JSPS Fellows
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| Allocation Type | Multi-year Fund |
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| Section | 外国 |
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| Review Section |
Basic Section 62010:Life, health and medical informatics-related
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| Research Institution | Institute of Physical and Chemical Research |
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Principal Investigator |
Skibbe Henrik 国立研究開発法人理化学研究所, 脳神経科学研究センター, ユニットリーダー (00735764)
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| Co-Investigator(Kenkyū-buntansha) |
DECROOCQ MEGHANE 国立研究開発法人理化学研究所, 脳神経科学研究センター, 外国人特別研究員
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| Project Period (FY) |
2023-11-15 – 2026-03-31
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| Project Status |
Granted (Fiscal Year 2023)
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| Budget Amount *help |
¥2,100,000 (Direct Cost: ¥2,100,000)
Fiscal Year 2025: ¥800,000 (Direct Cost: ¥800,000)
Fiscal Year 2024: ¥800,000 (Direct Cost: ¥800,000)
Fiscal Year 2023: ¥500,000 (Direct Cost: ¥500,000)
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| Keywords | multi-scale cGAN / microscopy imaging / neuron tracing / generative AI |
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| Outline of Research at the Start |
Advancing neural morphology analysis through automated generation of realistic, annotated neural network images, enhancing deep learning applications in biomedical imaging.
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| Outline of Annual Research Achievements |
We developed a new method leveraging conditional generative adversarial networks (cGANs) to generate diverse, high-resolution microscopy images for neuron tracing model training. The goal is to circumvent the limitations associated with the scarcity of annotated data for training machine learning models. The results were submitted as a full paper to the MIDL (Medical Imaging with Deep Learning) conference, where it has been accepted for presentation. The title of the paper is : "Multi-scale Stochastic Generation of Labelled Microscopy Images for Neuron Segmentation".
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| Current Status of Research Progress |
Current Status of Research Progress
1: Research has progressed more than it was originally planned.
Reason
After only a few months into the project, we have already achieved a significant milestone with a paper accepted at a competitive, international conference. This early success demonstrates the effectiveness of our approach.
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| Strategy for Future Research Activity |
We plan to advance our research by developing new tracing methods based on graph neural networks, which will incorporate global neuron topology into the learning process. Our goal is to seamlessly integrate these methods with our generative image synthesis algorithm, thereby creating a comprehensive pipeline for the automated analysis of neuron microscopy images. This integration is expected to significantly enhance the accuracy and efficiency of neuronal morphology analysis.
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