Kyushu University Academic Staff Educational and Research Activities Database
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Ryoma Bise Last modified date:2019.06.28



Graduate School
Undergraduate School
Other Organization
Other


E-Mail
Homepage
http://human.ait.kyushu-u.ac.jp/~bise/index-en.html
my website .
Phone
092-802-3574
Fax
092-802-3600
Academic Degree
Interdisciplinary Information Studies
Field of Specialization
Computer Vision, Bio medical image analysis, bio image informatics, pattern recognition
Research
Research Interests
  • Computer Vision, Bioimage informatics, Biomedical Image Analysis, Pattern Recognition, Image recoginition, Image processing
    keyword : Computer Vision, Bioimage informatics, Biomedical Image Analysis, Pattern Recognition, Image recoginition, Image processing
    2017.04~2019.03.
Current and Past Project
  • In the biological research, cell and molecular level biological phenomena is observed as 2D and 4D(3D+time) images by new imaging technologies to elucidate the principle of biological phenomena. In such research, quantification and data analysis technologies for the biological images captured in a state similar to in vivo are important. However, in vivo, observation targets such as cells and molecules are distributed in a high density. This makes it difficult to quantify the metrics of behaviors and shapes from such images, and this is critical in biological research.
    In this research, we are developing multi-object tracking methods for realizing robustness and versatility in high density, and we will apply the tracking methods and data analysis techniques to analyze the behaviors of cells and molecules. This makes the objective data analysis and scale up to big data easy. We believe that this will contribute to innovation in the biological field.
  • With the arrival of super-aging society, there is increasing demand for technical support to enable people to continue working while preserving their health and beauty. NII has participated in ImPACT to realize an early diagnosis of disease, and inspection of the internal structure, with advances in photo-acoustic imaging, which performs real-time 3D visualization of changes in properties and functions inside human bodies and substances, non-invasively and non-destructively. The photo-acoustic system is a promising new technology that integrates state-of-the-art laser and ultrasound technologies, where 3D structures of objects can be reconstructed by sensing emitted ultrasound from the objects that absorb near-infrared irradiation. It enables to image the state of the human body and objects whose insides are not visible, non-invasively and non-destructively. In this research, we develop computer-vision technologies to obtain clear images and extract bio-image features to support a diagnosis. For example, we proposed a registration method to generate high-quality 3D volumes in which vessels become clearly visible by aligning shot-volumes that are misaligned by body motions. We are also developing a technology that automatically models vascular structures, which helps in understanding blood vessel conditions strongly related to illnesses.
Academic Activities
Presentations
1. J. Hayashida, R. Bise, Cell Tracking with Deep Learning for Cell Detection and Motion Estimation in Low-Frame-Rate, International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI2019), 2019.10.
2. K. Nishimura, E.D. Ker, R. Bise, Weakly Supervised Cell Segmentation in Dense by Propagating from Detection Map, International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI2019), 2019.10.
3. H. Tokunaga, Y. Teramoto, A. Yoshizawa, R. Bise, Adaptive Weighting Multi-Field-of-View CNN for Semantic Segmentation in Pathology, IEEE Conference on Computer Vision and Pattern Recognition, 2019.06.
4. Mihoko Shimano, Ryoma Bise, Yinqiang Zheng, Imari Sato, Separation of transmitted light and scattering components in transmitted microscopy, 20th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2017, 2017.09, In transmitted light microscopy, a specimen tends to be observed as unclear. This is caused by a phenomenon that an image sensor captures the sum of these scattered light rays traveled from different paths due to scattering. To cope with this problem, we propose a novel computational photography approach for separating directly transmitted light from the scattering light in a transmitted light microscope by using high-frequency lighting. We first investigated light paths and clarified what types of light overlap in transmitted light microscopy. The scattered light can be simply represented and removed by using the difference in observations between focused and unfocused conditions, where the high-frequency illumination becomes homogeneous. Our method makes a novel spatial multiple-spectral absorption analysis possible, which requires absorption coefficients to be measured in each spectrum at each position. Experiments on real biological tissues demonstrated the effectiveness of our method..
5. Lin Gu, Yinqiang Zheng, Ryoma Bise, Imari Sato, Nobuaki Imanishi, Sadakazu Aiso, Semi-supervised learning for biomedical image segmentation via forest oriented super pixels(voxels), 20th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2017, 2017.09, In this paper, we focus on semi-supervised learning for biomedical image segmentation, so as to take advantage of huge unlabelled data. We observe that there usually exist some homogeneous connected areas of low confidence in biomedical images, which tend to confuse the classifier trained with limited labelled samples. To cope with this difficulty, we propose to construct forest oriented super pixels(voxels) to augment the standard random forest classifier, in which super pixels(voxels) are built upon the forest based code. Compared to the state-of-the-art, our proposed method shows superior segmentation performance on challenging 2D/3D biomedical images. The full implementation (based on Matlab) is available at https://github.com/lingucv/ssl_superpixels..
Membership in Academic Society
  • Information Processing Society of Japan
  • IEEE