| Budget Amount *help |
¥16,380,000 (Direct Cost: ¥12,600,000、Indirect Cost: ¥3,780,000)
Fiscal Year 2019: ¥3,510,000 (Direct Cost: ¥2,700,000、Indirect Cost: ¥810,000)
Fiscal Year 2018: ¥3,380,000 (Direct Cost: ¥2,600,000、Indirect Cost: ¥780,000)
Fiscal Year 2017: ¥3,380,000 (Direct Cost: ¥2,600,000、Indirect Cost: ¥780,000)
Fiscal Year 2016: ¥6,110,000 (Direct Cost: ¥4,700,000、Indirect Cost: ¥1,410,000)
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| Outline of Final Research Achievements |
Using deep learning, we developed an image processing algorithm to identify nuclei from the 4-dimensional fluorescence microscopic images of mouse embryo development. The existing analysis of cell dynamics in mouse embryogenesis has a problem that the accuracy of nuclear identification after 16 cell stages is very low for the 4D microscopy images. In this research, we developed a nuclear identification algorithm using deep learning, which has recently attracted attention as a powerful technique in image analysis. Our algorithm performed accurate nuclear identification up to the 50 cell stage to obtain quantitative criteria that can evaluate the quality of embryos.
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