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Ryoma Bise Last modified date：2022.06.21

Associate Professor / Data science
Department of Advanced Information Technology
Faculty of Information Science and Electrical Engineering


Graduate School
Department of Advanced information Technology
Graduate School of Information Science and Electrical Engineering
Undergraduate School
Department of Electrical Engineering and Computer Science
School of Engineering
Other Organization
Other


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Homepage
https://kyushu-u.pure.elsevier.com/en/persons/ryoma-bise
　Reseacher Profiling Tool　Kyushu University Pure
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
Country of degree conferring institution (Overseas)
No
Field of Specialization
Computer Vision, Bio medical image analysis, bio image informatics, pattern recognition
Total Priod of education and research career in the foreign country
02years05months
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
Papers
 Show All Papers >>
1. H. Cho, K. Nishimura, K. Watanabe, and R. Bise, Effective Pseudo-Labeling based on Heatmap for Unsupervised Domain Adaptation in Cell Detection, Medical Image Analysis, 10.1016/j.media.2022.102436, 102436-102449, 2022.04.
2. Nishimura Kazuya, Wang, K.C., Watanabe, Bise Ryoma, Weakly Supervised Cell Instance Segmentation Under Various Conditions, Medical Image Analysis, https://doi.org/10.1016/j.media.2021.102182, 2021.10, Cell instance segmentation is important in biomedical research. For living cell analysis, microscopy images are captured under various conditions (e.g., the type of microscopy and type of cell). Deep-learning-based methods can be used to perform instance segmentation if sufficient annotations of individual cell boundaries are prepared as training data. Generally, annotations are required for each condition, which is very time-consuming and labor-intensive. To reduce the annotation cost, we propose a weakly supervised cell instance segmentation method that can segment individual cell regions under various conditions by only using rough cell centroid positions as training data. This method dramatically reduces the annotation cost compared with the standard annotation method of supervised segmentation. We demonstrated the efficacy of our method on various cell images; it outperformed several of the conventional weakly-supervised methods on average. In addition, we demonstrated that our method can perform instance cell segmentation without any manual annotation by using pairs of phase contrast and fluorescence images in which cell nuclei are stained as training data..
3. Shota Harada, Ryoma Bise, Hideaki Hayashi, Kiyohito Tanaka, and Seiichi Uchida, Soft and Self Constrained Clustering for Group-Based Labeling, Medical Image Analysis, https://doi.org/10.1016/j.media.2021.102097, 2021.05, When using deep neural networks in medical image classification tasks, it is mandatory to prepare a large-scale labeled image set, and this often requires significant effort by medical experts. One strategy to reduce the labeling cost is group-based labeling, where image samples are clustered and then a label is attached to each cluster. The efficiency of this strategy depends on the purity of the clusters. Constrained clustering is an effective way to improve the purity of the clusters if we can give appropriate must-links and cannot-links as constraints. However, for medical image clustering, the conventional constrained clustering methods encounter two issues. The first issue is that constraints are not always appropriate due to the gap between semantic and visual similarities. The second issue is that attaching constraints requires extra effort from medical experts. To deal with the first issue, we propose a novel soft-constrained clustering method, which has the ability to ignore inappropriate constraints. To deal with the second issue, we propose a self-constrained clustering method that utilizes prior knowledge about the target images to set the constraints automatically. Experiments with the endoscopic image datasets demonstrated that the proposed methods give clustering results with higher purity..
4. K. Nishimura, J. Hayashida, C. Wang, D.F.E. Ker, and R. Bise, Weakly-Supervised Cell Tracking via Backward-and-Forward Propagation, 16th European Conference on Computer Vision (ECCV2020), 2020, https://doi.org/10.1007/978-3-030-58610-2_7, pp.104-pp.121, 2020.08, [URL], We propose a weakly-supervised cell tracking method that can train a convolutional neural network (CNN) by using only the annotation of “cell detection” (i.e., the coordinates of cell positions) without association information, in which cell positions can be easily obtained by nuclear staining. First, we train co-detection CNN that detects cells in successive frames by using weak-labels. Our key assumption is that co-detection CNN implicitly learns association in addition to detection. To obtain the association, we propose a backward-and-forward propagation method that analyzes the correspondence of cell positions in the outputs of co-detection CNN. Experiments demonstrated that the proposed method can associate cells by analyzing co-detection CNN. Even though the method uses only weak supervision, the performance of our method was almost the same as the state-of-the-art supervised method. Code is publicly available in https://github.com/naivete5656/WSCTBFP..
5. H. Tokunaga, B.K. Iwana, Y. Teramoto, A. Yoshizawa, and R. Bise, Negative Pseudo Labeling using Class Proportion for Semantic Segmentation in Pathology, 16th European Conference on Computer Vision (ECCV2020), 2020, 2020.08, We propose a weakly-supervised cell tracking method that can train a convolutional neural network (CNN) by using only the annotation of "cell detection" (i.e., the coordinates of cell positions) without association information, in which cell positions can be easily obtained by nuclear staining. First, we train a co-detection CNN that detects cells in successive frames by using weak-labels. Our key assumption is that the co-detection CNN implicitly learns association in addition to detection. To obtain the association information, we propose a backward-and-forward propagation method that analyzes the correspondence of cell positions in the detection maps output of the co-detection CNN. Experiments demonstrated that the proposed method can match positions by analyzing the co-detection CNN. Even though the method uses only weak supervision, the performance of our method was almost the same as the state-of-the-art supervised method..
6. J. Hayashida, K. Nishimura and R. Bise, MPM: Joint Representation of Motion and Position Map for Cell Tracking, IEEE CVPR2020, https://doi.org/10.1109/CVPR42600.2020.00388, 3822-3831, 2020.06, Conventional cell tracking methods detect multiple cells in each frame (detection) and then associate the detection results in successive time-frames (association). Most cell tracking methods perform the association task independently from the detection task. However, there is no guarantee of preserving coherence between these tasks, and lack of coherence may adversely affect tracking performance. In this paper, we propose the Motion and Position Map (MPM) that jointly represents both detection and association for not only migration but also cell division. It guarantees coherence such that if a cell is detected, the corresponding motion flow can always be obtained. It is a simple but powerful method for multi-object tracking in dense environments. We compared the proposed method with current tracking methods under various conditions in real biological images and found that it outperformed the state-of-the-art (+5.2\% improvement compared to the second-best)..
7. H. Tokunaga, Y. Teramoto, A. Yoshizawa, R. Bise, Adaptive Weighting Multi-Field-of-View CNN for Semantic Segmentation in Pathology, IEEE CVPR2019, 10.1109/CVPR.2019.01288, 2019.06.
8. Ryoma Bise, Yoichi Sato, Cell Detection From Redundant Candidate Regions Under Nonoverlapping Constraints, IEEE Transactions on Medical Imaging, 10.1109/TMI.2015.2391095, 34, 7, 1417-1427, 2015.07, Cell detection in microscopy images is essential for automated cell behavior analysis including cell shape analysis and cell tracking. Robust cell detection in high-density and low-contrast images is still challenging since cells often touch and partially overlap, forming a cell cluster with blurry intercellular boundaries. In such cases, current methods tend to detect multiple cells as a cluster. If the control parameters are adjusted to separate the touching cells, other problems often occur: a single cell may be segmented into several regions, and cells in low-intensity regions may not be detected. To solve these problems, we first detect redundant candidate regions, which include many false positives but in turn very few false negatives, by allowing candidate regions to overlap with each other. Next, the score for how likely the candidate region contains the main part of a single cell is computed for each cell candidate using supervised learning. Then we select an optimal set of cell regions from the redundant regions under nonoverlapping constraints, where each selected region looks like a single cell and the selected regions do not overlap. We formulate this problem of optimal region selection as a binary linear programming problem under nonoverlapping constraints. We demonstrated the effectiveness of our method for several types of cells in microscopy images. Our method performed better than five representative methods, achieving an F-measure of over 0.9 for all data sets. Experimental application of the proposed method to 3-D images demonstrated that also works well for 3-D cell detection..
Presentations
 Show All 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. 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..
5. 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..
Membership in Academic Society
  • IEICE
  • Information Processing Society of Japan
  • IEEE


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