In biomedical imaging, evaluation of imaging data has long been performed by human intuitive vision and exhaustive manual analyses. However, quantitative and reproducible methods are required for the analysis of large-scale multidimensional image data. This study aims to develop automated methods for the analysis of structure and motion from multidimensional image data. By introducing computer vision techniques, we develop versatile image analysis software for various biological phenomena at the cellular, tissue and organism levels.
2019
Tamada A. (2019) Chiral Neuronal Motility: The Missing Link between Molecular Chirality and Brain Asymmetry. Symmetry, 11: 102.
doi: 10.3390/sym11010102.
2018
Kawasaki A., Okada M., Tamada A., Okuda S., Nozumi M., Ito Y., Kobayashi D., Yamasaki T., Yokoyama R., Shibata T., Nishina H., Yoshida Y., Fujii Y., Takeuchi K., Igarashi M. (2019) Growth Cone Phosphoproteomics Reveals that GAP-43 Phosphorylated by JNK Is a Marker of Axon Growth and Regeneration. iScience, 4: 190-203.
doi: 10.1016/j.isci.2018.05.019.
2017
Tamada A., Igarashi M. (2017) Revealing chiral cell motility by 3D Riesz transform-differential interference contrast microscopy and computational kinematic analysis. Nature Communications, 8: 2194.
doi:10.1038/s41467-017-02193-w.
