Combining molecular design and photonics dramatically improves bioimaging technology and such ResonanceBio approach has recently attracted a considerable attention in modern science communities. On the other hand, it is difficult to process/manage the images generated by such new imaging technology via conventional techniques. Therefore, we are working on new bioimage processing techniques for ResonanceBio images such as cloud-based image database for unified image management, image processing software including GUI for biologists, and new algorithms/models for the images. We are also planing to make our results available not only for this project but also for general academic use.
Our research consists of the following four subjects.
- Image processing algorithms: objective pattern analysis and fundamental methodology.
- 3D image reconstruction: computational methods for imaging and image reconstruction.
- Software and systems: image processing software (VCAT) and cloud-based system.
- Facilitating interdisciplinary research: image processing algorithm contest, workshops, and conferences.
Our research is based on cutting-edge image processing techniques and new algorithms, and is attempt to unify management, processing, and analysis of the images which are very different from conventional images in terms of its features and amounts. Moreover, we hope that our research will play an important role in combining biology, optics, chemistry, and computer science by collaboratingwith the other teams in this project.
2019
Kitrungrotsakul T., Han X.-H., Iwamoto Y., Takemoto S., Yokota H., Ipponjima S., Nemoto T., Wei X., Chen Y.-W. (2019) A Cascade of 2.5D CNN and Bidirectional CLSTM Network for Mitotic Cell Detection in 4D Microscopy Image. in IEEE/ACM Transactions on Computational Biology and Bioinformatics.
doi:10.1109/TCBB.2019.2919015.
Morita M., Shimokawa K., Nishimura M., Nakamura S., Tsujimura Y., Takemoto S., Tawara T., Yokota H., Wemler S., Miyamoto D., Ikeno H., Sato A., Furuichi T., Kobayashi N., Okumura Y., Yamaguchi Y., Okamura-Oho Y. (2019) ViBrism DB: an interactive search and viewer platform for 2D/3D anatomical images of gene expression and co-expression networks, Nucleic Acids Research, 47(D1): D859–D866.
doi:10.1093/nar/gky951.
2018
Sakane A., Yoshizawa S., Yokota H., Sasaki T. (2018) Dancing Styles of Collective Cell Migration: Image-Based Computational Analysis of JRAB/MICAL-L2. Frontiers in Cell and Developmental Biology, 6: 4.
doi:10.3389/fcell.2018.00004.
2017
Mimura Y., Takemoto S., Tachibana T., Ogawa Y., Nishimura M., Yokota H., Imamoto N. (2017) A statistical image analysis framework for pore-free islands derived from heterogeneity distribution of nuclear pore complexes, Scientific Reports, 7(1): 16315.
doi: 10.1038/s41598-017-16386-2.
2016
Sakane A., Yoshizawa S., Nishimura M., Tsuchiya Y., Matsushita N., Miyake K., Horikawa K., Imoto I., Mizuguchi C., Saito H., Ueno T., Matsushita S., Haga H., Deguchi S., Mizuguchi K., Yokota H., Sasaki T. (2016)Conformational plasticity of JRAB/MICAL-L2 provides “law and order” in collective cell migration. Mol. Biol. Cell, 27: 3095-3108.
doi: 10.1091/mbc.E16-05-0332.
2015
Yamashita N., Morita M., Legant W. R., Chen B.-C., Betzig E., Yokota H., Mimori-Kiyosue Y. (2015) Three-dimensional tracking of plus-tips by lattice light-sheet microscopy permits the quantification of microtubule growth trajectories within the mitotic apparatus. J Biomed. Opt., 20: 101206.
doi: 10.1117/1.JBO.20.10.101206.
Ono M., Akuzawa H., Nambo Y., Hirano Y., Kimura J., Takemoto S., Nakamura S., Yokota H., Himeno R., Higuchi T, Ohtaki T., Tsumagari S. (2015) Analysis of the equine ovarian structure during the first twelve months of life by three-dimensional internal structure microscopy. J.Vet. Med. Sci., 77: 1599-1603.
doi: 10.1292/jvms.14-0539.