| Bise Ryoma | Last modified date：2023.12.04 |
Professor /
Data science /
Department of Advanced Information Technology /
Faculty of Information Science and Electrical Engineering
Papers
| 1. | Shinnosuke Matsuo, Ryoma Bise, Seiichi Uchida, Daiki Suehiro, Learning From Label Proportion with Online Pseudo-Label Decision by Regret Minimization, IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 10.1109/ICASSP49357.2023.10097069, 2023.06. |
| 2. | Yuki Shigeyasu, Shota Harada, Kengo Araki, Akihiko Yoshizawa, Kazuhiro Terada, Ryoma Bise, Spatial Distribution-based Pseudo Labeling for Pathological Image Segmentation, IEEE International Symposium on Biomedical Imaging (ISBI), 2023.04. |
| 3. | Xiaoqing Liu, Kengo Araki, Shota Harada, Akihiko Yoshizawa, Kazuhiro Terada, Mariyo Kurata, Naoki Nakajima, Hiroyuki Abe, Tetsuo Ushiku, Ryoma Bise, Cluster Entropy: Active Domain Adaptation in Pathological Image Segmentation, IEEE International Symposium on Biomedical Imaging (ISBI), 2023.04. |
| 4. | Xiaoqing Liu, Kenji Ono, Ryoma Bise, Mixing Data Augmentation with Preserving Foreground Regions in Medical Image Segmentation, IEEE International Symposium on Biomedical Imaging (ISBI), 2023.04. |
| 5. | Shota Harada, Ryoma Bise, Kengo Araki, Akihiko Yoshizawa, Kazuhiro Terada, Mariyo Kurata, Naoki Nakajima, Hiroyuki Abe, Tetsuo Ushiku, Seiichi Uchida, Cluster-Guided Semi-Supervised Domain Adaptation for Imbalanced Medical Image Classification, IEEE International Symposium on Biomedical Imaging (ISBI), 2023.04. |
| 6. | Takanori Asanomi, Kazuya Nishimura, Ryoma Bise, Multi-Frame Attention With Feature-Level Warping for Drone Crowd Tracking, IEEE/CVF Winter Conference on Applications of Computer Vision, 10.1109/WACV56688.2023.00171, 1664-1673, 2023.01. |
| 7. | Kazuya Nishimura, Ryoma Bise, Weakly Supervised Cell-Instance Segmentation With Two Types of Weak Labels by Single Instance Pasting, IEEE/CVF Winter Conference on Applications of Computer Vision, 10.1109/WACV56688.2023.00320, 3185-3194, 2023.01. |
| 8. | Kazuki Miyama, Ryoma Bise, Satoshi Ikemura, Kazuhiro Kai, Masaya Kanahori, Shinkichi Arisumi, Taisuke Uchida, Yasuharu Nakashima, Seiichi Uchida, Deep learning-based automatic-bone-destruction-evaluation system using contextual information from other joints, Arthritis Research & Therapy, https://doi.org/10.1186/s13075-022-02914-7, 24, 1, 2022.10. |
| 9. | Takanori Asanomi, Kazuya Nishimura, Heon Song, Junya Hayashida, Hiroyuki Sekiguchi, Takayuki Yagi, Imari Sato, and Ryoma Bise, Unsupervised Deep Robust Non-Rigid Alignment by Low-Rank Loss and Multi-Input Attention , International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI2022), 2022.09. |
| 10. | T. Kadota, H Hayashi, R Bise, K Tanaka, S Uchida, Deep Bayesian Active-learning-to-rank for Endoscopic Image Data, 26th Conference on Medical Image Understanding and Analysis 2022, 2022.07. |
| 11. | T. Sugimoto, H. Ito, Y. Teramoto, A. Yoshizawa, and R. Bise, Multi-Class Cell Detection Using Modified Self-Attention, IEEE CVPR Workshop CVMI, 2022.06. |
| 12. | 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. |
| 13. | T. Kadota, K Abe, R Bise, T Kawamura, N Sakiyama, K Tanaka, S Uchida, Automatic Estimation of Ulcerative Colitis Severity by Learning to Rank With Calibration, IEEE Access, 10.1109/ACCESS.2022.3155769, 10, 25688-25695, 2022.03. |
| 14. | J Hayashida, K Nishimura, R Bise, Consistent Cell Tracking in Multi-Frames with Spatio-Temporal Context by Object-Level Warping Loss, IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 10.1109/WACV51458.2022.00182, 1727-1736, 2022.01. |
| 15. | Araki Kengo、Rokutan-Kurata Mariyo、Terada Kazuhiro、Yoshizawa Akihiko、Bise Ryoma, Patch-Based Cervical Cancer Segmentation using Distance from Boundary of Tissue, International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 10.1109/EMBC46164.2021.9630809, 3328-3331, 2021.11. |
| 16. | R Kikkawa, H Kajita, N Imanishi, S Aiso, R Bise, Unsupervised Body Hair Detection by Positive-Unlabeled Learning in Photoacoustic Image, International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 10.1109/EMBC46164.2021.9630720, 3349-3352, 2021.11. |
| 17. | Hyeonwoo Cho, Kazuya Nishimura, Kazuhide Watanabe, and Ryoma Bise, Cell Detection in Domain Shift Problem Using Pseudo-Cell-Position Heatmap, International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI2021), 2021.10, The domain shift problem is an important issue in automatic cell detection. A detection network trained with training data under a specific condition (source domain) may not work well in data under other conditions (target domain). We propose an unsupervised domain adaptation method for cell detection using the pseudo-cell-position heatmap, where a cell centroid becomes a peak with a Gaussian distribution in the map. In the prediction result for the target domain, even if a peak location is correct, the signal distribution around the peak often has a non-Gaussian shape. The pseudo-cell-position heatmap is re-generated using the peak positions in the predicted heatmap to have a clear Gaussian shape. Our method selects confident pseudo-cell-position heatmaps using a Bayesian network and adds them to the training data in the next iteration. The method can incrementally extend the domain from the source domain to the target domain in a semi-supervised manner. In the experiments using 8 combinations of domains, the proposed method outperformed the existing domain adaptation methods.. |
| 18. | 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.. |
| 19. | Shota Harada, Ryoma Bise, Hideaki Hayashi, Kiyohito Tanaka and Seiichi Uchida, Order-Guided Disentangled Representation Learning for Ulcerative Colitis Classification with Limited Labels, International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI2021), https://doi.org/10.1007/978-3-030-87196-3_44, 471-480, 2021.09. |
| 20. | Fujii Kazuma、Suehiro Daiki、Nishimura Kazuya、Bise Ryoma, Cell Detection from Imperfect Annotation by Pseudo Label Selection Using P-classification, International Conference on Medical Image Computing and Computer-Assisted Intervention(MICCAI2021), 10.1007/978-3-030-87237-3_41, 425-434, 2021.09. |
| 21. | Nishimura Kazuya、Cho Hyeonwoo、Bise Ryoma, Semi-supervised Cell Detection in Time-Lapse Images Using Temporal Consistency, International Conference on Medical Image Computing and Computer-Assisted Intervention(MICCAI2021), 10.1007/978-3-030-87237-3_36, 373-383, 2021.09. |
| 22. | 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.. |
| 23. | M. Shimano, Y. Asano, S. Ishihara, R. Bise, and I. Sato, Imaging Scattering Characteristics of Tissue in Transmitted Microscopy, International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI2020), 2020.10. |
| 24. | A. Kondow, K. Ohnuma, Y. Kamei, A. Taniguchi, R. Bise, Y. Sato, H. Yamaguchi, S. Nonaka, K. Hashimoto, Light-sheet microscopy-based 3D single-cell tracking assay revealed a correlation between cell cycle and the beginning of endodermal cell internalization in zebrafish early development, Development Growth and Differentiation, 2020, 2020.10. |
| 25. | 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.. |
| 26. | 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.. |
| 27. | K. Nishimura, and R. Bise, Spatial-Temporal Mitosis Detection in Phase-Contrast Microscopy Via Likelihood Map Estimation by 3DCNN, 42st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2020.07, Automated mitotic detection in time-lapse phase-contrast microscopy provides us much information for cell behavior analysis, and thus several mitosis detection methods have been proposed. However, these methods still have two problems; 1) they cannot detect multiple mitosis events when there are closely placed. 2) they do not consider the annotation gaps, which may occur since the appearances of mitosis cells are very similar before and after the annotated frame. In this paper, we propose a novel mitosis detection method that can detect multiple mitosis events in a candidate sequence and mitigate the human annotation gap via estimating a spatiotemporal likelihood map by 3DCNN. In this training, the loss gradually decreases with the gap size between ground truth and estimation. This mitigates the annotation gaps. Our method outperformed the compared methods in terms of F1- score using a challenging dataset that contains the data under four different conditions.. |
| 28. | 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).. |
| 29. | Maho Ueda, Susumu Saito, Teruasa Murata, Tomoko Hirano, Ryoma Bise, Kenji Kabashima, Shigehiko Suzuki, Combined multiphoton imaging and biaxial tissue extension for quantitative analysis of geometric fiber organization in human reticular dermis, Scientific reports, 10.1038/s41598-019-47213-5, 9, 1, 2019.12, The geometric organization of collagen fibers in human reticular dermis and its relationship to that of elastic fibers remain unclear. The tight packing and complex intertwining of dermal collagen fibers hinder accurate analysis of fiber orientation. We hypothesized that combined multiphoton microscopy and biaxial extension could overcome this issue. Continuous observation of fresh dermal sheets under biaxial extension revealed that the geometry of the elastic fiber network is maintained during expansion. Full-thickness human thigh skin samples were biaxially extended and cleared to visualize the entire reticular dermis. Throughout the dermis, collagen fibers straightened with increased inter-fiber spaces, making them more clearly identifiable after extension. The distribution of collagen fibers was evaluated with compilation of local orientation data. Two or three modes were confirmed in all superficial reticular layer samples. A high degree of local similarities in the direction of collagen and elastic fibers was observed. More than 80% of fibers had directional differences of ≤15°, regardless of layer. Understanding the geometric organization of fibers in the reticular dermis improves the understanding of mechanisms underlying the pliability of human skin. Combined multiphoton imaging and biaxial extension provides a research tool for studying the fibrous microarchitecture of the skin.. |
| 30. | Junya Hayashida, Ryoma Bise, Cell tracking with deep learning for cell detection and motion estimation in low-frame-rate, 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019, 10.1007/978-3-030-32239-7_44, 397-405, 2019.11, Cell behavior analysis in high-throughput biological experiments is important for research and discovery in biology and medicine. To perform the high-throughput experiments, it requires to capture images in low frame rate in order to record images on multi-points. In such a low frame rate image sequence, movements of cells between successive frames are often larger than distances to nearby cells, and thus current methods based on proximity do not work properly. In this study, we propose a cell tracking method that enables to track cells in low frame rate by simultaneously estimating all of the cell motions in successive frames. In the experiments under dense conditions in low frame rate, our method outperformed the other methods.. |
| 31. | Ryoma Bise, Kentaro Abe, Hideaki Hayashi, Kiyohito Tanaka, Seiichi Uchida, Efficient Soft-Constrained Clustering for Group-Based Labeling, 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019 , 10.1007/978-3-030-32254-0_47, 421-430, 2019.10, We propose a soft-constrained clustering method for group-based labeling of medical images. Since the idea of group-based labeling is to attach the label to a group of samples at once, we need to have groups (i.e., clusters) with high purity. The proposed method is formulated to achieve high purity even for difficult clustering tasks such as medical image clustering, where image samples of the same class are often very distant in their feature space. In fact, those images degrade the performance of conventional constrained clustering methods. Experiments with an endoscopy image dataset demonstrated that our method outperformed various state-of-the-art methods.. |
| 32. | Kazuya Nishimura, Dai Fei Elmer Ker, Ryoma Bise, Weakly supervised cell instance segmentation by propagating from detection response, 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019, 10.1007/978-3-030-32239-7_72, 649-657, 2019.10, Cell shape analysis is important in biomedical research. Deep learning methods may perform to segment individual cells if they use sufficient training data that the boundary of each cell is annotated. However, it is very time-consuming for preparing such detailed annotation for many cell culture conditions. In this paper, we propose a weakly supervised method that can segment individual cell regions who touch each other with unclear boundaries in dense conditions without the training data for cell regions. We demonstrated the efficacy of our method using several data-set including multiple cell types captured by several types of microscopy. Our method achieved the highest accuracy compared with several conventional methods. In addition, we demonstrated that our method can perform without any annotation by using fluorescence images that cell nuclear were stained as training data. Code is publicly available in https://github.com/naivete5656/WSISPDR.. |
| 33. | S. Harada, H. Hayashi, R. Bise, K. Tanaka, Q. Meng, and S. Uchida, Endoscopic Image Clustering with Temporal Ordering Information Based on Dynamic Programming, 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2019., 10.1109/EMBC.2019.8857011, 2019.07. |
| 34. | D. Harada, R. Bise, H. Tokunaga, W. Ohyama, S. Oka, T. Fujimori, and S. Uchida, Scribbles for Metric Learning in Histological Image Segmentation, 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2019., 10.1109/EMBC.2019.8856465, 2019.07. |
| 35. | 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. |
| 36. | Ryo Kikkawa, Hiroyuki Sekiguchi, Itaru Tsuge, Susumu Saito, Ryoma Bise, Semi-supervised learning with structured knowledge for body hair detection in photoacoustic image, 16th IEEE International Symposium on Biomedical Imaging, ISBI 2019, 10.1109/ISBI.2019.8759249, 1411-1415, 2019.04, Photoacoustic (PA) imaging is a promising new imaging technology for non-invasively visualizing blood vessels inside biological tissues. In addition to blood vessels, body hairs are also visualized in PA imaging, and the body hair signals degrade the visibility of blood vessels. For learning a body hair classifier, the amount of real training and test data is limited, because PA imaging is a new modality. To address this problem, we propose a novel semi-supervised learning (SSL) method for extracting body hairs. The method effectively learns the discriminative model from small labeled training data and small unlabeled test data by introducing prior knowledge, of the orientation similarity among adjacent body hairs, into SSL. Experimental results using real PA data demonstrate that the proposed approach is effective for extracting body hairs as compared with several baseline methods.. |
| 37. | Susumu Saito, Ryoma Bise, Aya Yoshikawa, Hiroyuki Sekiguchi, Itaru Tsuge, Masakazu Toi, Digital artery deformation on movement of the proximal interphalangeal joint, Journal of Hand Surgery: European Volume, 10.1177/1753193418807833, 44, 2, 187-195, 2019.02, This study aimed to characterize in vivo human digital arteries in three-dimensions using photoacoustic tomography in order to understand the specific mechanism underlying arterial deformation associated with movement of the proximal interphalangeal joint. Three-dimensional morphological data were obtained on the radialis indicis artery (radial artery of the index finger) at different angles of the joint. The association between increased curvature of the deformation and the anatomical region was assessed. Characteristic morphological deformations in areas of major deformation were determined. The deformation of the artery was characterized by three consecutive curves in juxta-articular regions, which were particularly noticeable when the joint was flexed at an angle of ≥ 60°. The change in the curvature of the deformation during 30°–90° of flexion was lower in middle-aged individuals than in young individuals. Better understanding of the mechanism underlying deformation of the digital arteries may contribute to advancements in flap procedures and rehabilitation strategies after digital artery repair.. |
| 38. | H Okawa, M Shimano, Y Asano, R Bise, K Nishino, I Sato, Estimation of Wetness and Color From A Single Multispectral Image, IEEE transactions on pattern analysis and machine intelligence, 10.1109/TPAMI.2019.2903496, 2019.02, [URL]. |
| 39. | Dai Fei Elmer Ker, Sungeun Eom, Sho Sanami, Ryoma Bise, Corinne Pascale, Zhaozheng Yin, Seung Il Huh, Elvira Osuna-Highley, Silvina N. Junkers, Casey J. Helfrich, Peter Yongwen Liang, Jiyan Pan, Soojin Jeong, Steven S. Kang, Jinyu Liu, Ritchie Nicholson, Michael F. Sandbothe, Phu T. Van, Anan Liu, Mei Chen, Takeo Kanade, Lee E. Weiss, Phil G. Campbell, Phase contrast time-lapse microscopy datasets with automated and manual cell tracking annotations, Scientific Data, 10.1038/sdata.2018.237, 5, 2019.01, Phase contrast time-lapse microscopy is a non-destructive technique that generates large volumes of image-based information to quantify the behaviour of individual cells or cell populations. To guide the development of algorithms for computer-aided cell tracking and analysis, 48 time-lapse image sequences, each spanning approximately 3.5 days, were generated with accompanying ground truths for C2C12 myoblast cells cultured under 4 different media conditions, including with fibroblast growth factor 2 (FGF2), bone morphogenetic protein 2 (BMP2), FGF2 + BMP2, and control (no growth factor). The ground truths generated contain information for tracking at least 3 parent cells and their descendants within these datasets and were validated using a two-tier system of manual curation. This comprehensive, validated dataset will be useful in advancing the development of computer-aided cell tracking algorithms and function as a benchmark, providing an invaluable opportunity to deepen our understanding of individual and population-based cell dynamics for biomedical research.. |
| 40. | Kentaro Kajiya, Ryoma Bise, Catharina Commerford, Imari Sato, Toyonobu Yamashita, Michael Detmar, Light-sheet microscopy reveals site-specific 3-dimensional patterns of the cutaneous vasculature and pronounced rarefication in aged skin, Journal of Dermatological Science, 10.1016/j.jdermsci.2018.07.006, 92, 1, 3-5, 2018.10. |
| 41. | 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, 10.1007/978-3-319-66185-8_2, 12-20, 2017.10, 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.. |
| 42. | Qiuyu Chen, Ryoma Bise, Lin Gu, Yinqiang Zheng, Imari Sato, Jenq Neng Hwang, Sadakazu Aiso, Nobuaki Imanishi, Virtual Blood Vessels in Complex Background Using Stereo X-Ray Images, 16th IEEE International Conference on Computer Vision Workshops, ICCVW 2017, 10.1109/ICCVW.2017.20, 99-106, 2017.07, We propose a fully automatic system to reconstruct and visualize 3D blood vessels in Augmented Reality (AR) system from stereo X-ray images with bones and body fat. Currently, typical 3D imaging technologies are expensive and carrying the risk of irradiation exposure. To reduce the potential harm, we only need to take two X-ray images before visualizing the vessels. Our system can effectively reconstruct and visualize vessels in following steps. We first conduct initial segmentation using Markov Random Field and then refine segmentation in an entropy based post-process. We parse the segmented vessels by extracting their centerlines and generating trees. We propose a coarse-to-fine scheme for stereo matching, including initial matching using affine transform and dense matching using Hungarian algorithm guided by Gaussian regression. Finally, we render and visualize the reconstructed model in a HoloLens based AR system, which can essentially change the way of visualizing medical data. We have evaluated its performance by using synthetic and real stereo X-ray images, and achieved satisfactory quantitative and qualitative results.. |
| 43. | 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, 10.1007/978-3-319-66182-7_80, 702-710, 2017.01, 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.. |
| 44. | Ryoma Bise, Yingqiang Zheng, Imari Sato, Masakazu Toi, Vascular registration in photoacoustic imaging by low-rank alignment via foreground,background and complement decomposition, Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 , 10.1007/978-3-319-46726-9_38, 326-334, 2016.10, Photoacoustic (PA) imaging has been gaining attention as a new imaging modality that can non-invasively visualize blood vessels inside biological tissues. In the process of imaging large body parts through multi-scan fusion,alignment turns out to be an important issue,since body motion degrades image quality. In this paper,we carefully examine the characteristics of PA images and propose a novel registration method that achieves better alignment while effectively decomposing the shot volumes into low-rank foreground (blood vessels),dense background (noise),and sparse complement (corruption) components on the basis of the PA characteristics. The results of experiments using a challenging real data-set demonstrate the efficacy of the proposed method,which significantly improved image quality,and had the best alignment accuracy among the state-of-the-art methods tested.. |
| 45. | Ryoma Bise, Imari Sato, Kentaro Kajiya, Toyonobu Yamashita, 3D Structure Modeling of Dense Capillaries by Multi-objects Tracking, 29th IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2016, 10.1109/CVPRW.2016.168, 1333-1341, 2016.06, A newly developed imaging technique called light-sheet laser microscopy imaging can visualize the detailed 3D structures of capillaries. Capillaries form complicated network structures in the obtained data, and this makes it difficult to model vessel structures by existing methods that implicitly assume simple tree structures for blood vessels. To cope with such dense capillaries with network structures, we propose to track the flow of blood vessels along a base-axis using a multiple-object tracking framework. We first track multiple blood vessels in cross-sectional images along a single axis to make the trajectories of blood vessels, and then connect these blood vessels to reveal their entire structures. This framework is efficient to track densely distributed vessels since it uses only a single cross-sectional plane. The network structure is then generated in the post-processing by connecting blood vessels on the basis of orientations of the trajectories. The results of experiments using a challenging real data-set demonstrate the efficacy of the proposed method, which are capable of modeling dense capillaries.. |
| 46. | Noriko Yasuda, Hidekazu Sekine, Ryoma Bise, Teruo Okano, Tatsuya Shimizu, Tracing behavior of endothelial cells promotes vascular network formation, Microvascular Research, 10.1016/j.mvr.2015.12.005, 105, 125-131, 2016.05, The in vitro formation of network structures derived from endothelial cells in grafts before transplantation contributes to earlier engraftment. In a previous study, endothelial cells migrated to form a net-shaped structure in co-culture. However, the specific network formation behavior of endothelial cells during migration remains unclear. In this study, we demonstrated the tracing behavior and cell cycle of endothelial cells using Fucci-labeled (Fluorescent Ubiquitination-based Cell Cycle Indicator) endothelial cells. Here, we observed the co-culture of Fucci-labeled human umbilical vein endothelial cells (HUVECs) together with normal human dermal fibroblasts (NHDFs) using time-lapse imaging and analyzed by multicellular concurrent tracking. In the G0/G1 period, HUVECs migrate faster than in the S/G2/M period, because G0/G1 is the mobile phase and S/G2/M is the proliferation phase in the cell cycle. When HUVECs are co-cultured, they tend to move randomly until they find existing tracks that they then follow to form clusters. Extracellular matrix (ECM) staining showed that collagen IV, laminin and thrombospondin deposited in accordance with endothelial cell networks. Therefore the HUVECs may migrate on the secreted ECM and exhibit tracing behavior, where the HUVECs migrate toward each other. These results suggested that ECM and a cell phase contributed to form a network by accelerating cell migration.. |
| 47. | 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.. |
| 48. | Ryoma Bise, Yoshitaka Maeda, Mee Hae Kim, Masahiro Kino-Oka, Cell tracking under high confluency conditions by candidate cell region detection-based association approach, 10th IASTED International Conference on Biomedical Engineering, BioMed 2013, 10.2316/P.2013.791-057, 554-561, 2013.09, Automated tracking of cell population is an important element of research and discovery in the biology field. In this paper, we propose a method that tracks cells under highly confluent conditions by using the candidate cell region detection-based association approach. Unlike conventional segmentation-based association tracking methods, the proposed method uses the tracking results from the previous frame to segment the cell regions at the current frame. First, candidate cell regions are detected, and while there may be many false positives, there are very few false negatives. Next, optimized detection results are selected from the candidate regions and associated with the tracking results of the previous frame by resolving a linear programming problem. We quantitatively evaluated the proposed method using a variety of sequences. Results showed that our method has a better tracking performance than conventional segmentation-based association methods.. |
| 49. | Alexandre J.S. Ribeiro, Steven Tottey, Richard W.E. Taylor, Ryoma Bise, Takeo Kanade, Stephen F. Badylak, Kris Noel Dahl, Mechanical characterization of adult stem cells from bone marrow and perivascular niches, Journal of Biomechanics, 10.1016/j.jbiomech.2012.01.032, 45, 7, 1280-1287, 2012.04, Therapies using adult stem cells often require mechanical manipulation such as injection or incorporation into scaffolds. However, force-induced rupture and mechanosensitivity of cells during manipulation is largely ignored. Here, we image cell mechanical structures and perform a biophysical characterization of three different types of human adult stem cells: bone marrow CD34+ hematopoietic, bone marrow mesenchymal and perivascular mesenchymal stem cells. We use micropipette aspiration to characterize cell mechanics and quantify deformation of subcellular structures under force and its contribution to global cell deformation. Our results suggest that CD34+ cells are mechanically suitable for injection systems since cells transition from solid- to fluid-like at constant aspiration pressure, probably due to a poorly developed actin cytoskeleton. Conversely, mesenchymal stem cells from the bone marrow and perivascular niches are more suitable for seeding into biomaterial scaffolds since they are mechanically robust and have developed cytoskeletal structures that may allow cellular stable attachment and motility through solid porous environments. Among these, perivascular stem cells cultured in 6% oxygen show a developed cytoskeleton but a more compliant nucleus, which can facilitate the penetration into pores of tissues or scaffolds. We confirm the relevance of our measurements using cell motility and migration assays and measure survival of injected cells. Since different types of adult stem cells can be used for similar applications, we suggest considering mechanical properties of stem cells to match optimal mechanical characteristics of therapies.. |
| 50. | Ryoma Bise, Takeo Kanade, Zhaozheng Yin, Seung il Huh, Automatic cell tracking applied to analysis of cell migration in wound healing assay., Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings, 6174-6179, 2011.12, The wound healing assay in vitro is widely used for research and discovery in biology and medicine. This assay allows for observing the healing process in vitro in which the cells on the edges of the artificial wound migrate toward the wound area. The influence of different culture conditions can be measured by observing the change in the size of the wound area. For further investigation, more detailed measurements of the cell behaviors are required. In this paper, we present an application of automatic cell tracking in phase-contrast microscopy images to wound healing assay. The cell behaviors under three different culture conditions have been analyzed. Our cell tracking system can track individual cells during the healing process and provide detailed spatio-temporal measurements of cell behaviors. The application demonstrates the effectiveness of automatic cell tracking for quantitative and detailed analysis of the cell behaviors in wound healing assay in vitro.. |
| 51. | Dai Fei Elmer Ker, Lee E. Weiss, Silvina N. Junkers, Mei Chen, Zhaozheng Yin, Michael F. Sandbothe, Seung Il Huh, Sungeun Eom, Ryoma Bise, Elvira Osuna-Highley, Takeo Kanade, Phil G. Campbell, An engineered approach to stem cell culture Automating the decision process for real-time adaptive subculture of stem cells, PLoS One, 10.1371/journal.pone.0027672, 6, 11, 2011.11, Current cell culture practices are dependent upon human operators and remain laborious and highly subjective, resulting in large variations and inconsistent outcomes, especially when using visual assessments of cell confluency to determine the appropriate time to subculture cells. Although efforts to automate cell culture with robotic systems are underway, the majority of such systems still require human intervention to determine when to subculture. Thus, it is necessary to accurately and objectively determine the appropriate time for cell passaging. Optimal stem cell culturing that maintains cell pluripotency while maximizing cell yields will be especially important for efficient, cost-effective stem cell-based therapies. Toward this goal we developed a real-time computer vision-based system that monitors the degree of cell confluency with a precision of 0.791±0.031 and recall of 0.559±0.043. The system consists of an automated phase-contrast time-lapse microscope and a server. Multiple dishes are sequentially imaged and the data is uploaded to the server that performs computer vision processing, predicts when cells will exceed a pre-defined threshold for optimal cell confluency, and provides a Web-based interface for remote cell culture monitoring. Human operators are also notified via text messaging and e-mail 4 hours prior to reaching this threshold and immediately upon reaching this threshold. This system was successfully used to direct the expansion of a paradigm stem cell population, C2C12 cells. Computer-directed and human-directed control subcultures required 3 serial cultures to achieve the theoretical target cell yield of 50 million C2C12 cells and showed no difference for myogenic and osteogenic differentiation. This automated vision-based system has potential as a tool toward adaptive real-time control of subculturing, cell culture optimization and quality assurance/quality control, and it could be integrated with current and developing robotic cell cultures systems to achieve complete automation.. |
| 52. | Seungil Huh, Sungeun Eom, Ryoma Bise, Zhaozheng Yin, Takeo Kanade, Mitosis detection for stem cell tracking in phase-contrast microscopy images, 2011 8th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI'11, 10.1109/ISBI.2011.5872832, 2121-2127, 2011.11, Automated visual-tracking systems of stem cell populations in vitro allow for high-throughput analysis of time-lapse phase-contrast microscopy. In these systems, detection of mitosis, or cell division, is critical to tracking performance as mitosis causes branching of the trajectory of a mother cell into the two trajectories of its daughter cells. Recently, one mitosis detection algorithm showed its success in detecting the time and location that two daughter cells first clearly appear as a result of mitosis. This detection result can therefore helps trajectories to correctly bifurcate and the relations between mother and daughter cells to be revealed. In this paper, we demonstrate that the functionality of this recent mitosis detection algorithm significantly improves state-of-the-art cell tracking systems through extensive experiments on 48 C2C12 myoblastic stem cell populations under four different conditions.. |
| 53. | Ryoma Bise, Zhaozheng Yin, Takeo Kanade, Reliable cell tracking by global data association, 2011 8th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI'11, 10.1109/ISBI.2011.5872571, 1004-1010, 2011.11, Automated cell tracking in populations is important for research and discovery in biology and medicine. In this paper, we propose a cell tracking method based on global spatio-temporal data association which considers hypotheses of initialization, termination, translation, division and false positive in an integrated formulation. Firstly, reliable tracklets (i.e., short trajectories) are generated by linking detection responses based on frame-by-frame association. Next, these tracklets are globally associated over time to obtain final cell trajectories and lineage trees. During global association, tracklets form tree structures where a mother cell divides into two daughter cells. We formulate the global association for tree structures as a maximum-a-posteriori (MAP) problem and solve it by linear programming. This approach is quantitatively evaluated on sequences with thousands of cells captured over several days.. |
| 54. | Seungil Huh, Dai Fei Elmer Ker, Ryoma Bise, Mei Chen, Takeo Kanade, Automated mitosis detection of stem cell populations in phase-contrast microscopy images, IEEE Transactions on Medical Imaging, 10.1109/TMI.2010.2089384, 30, 3, 586-596, 2011.03, Due to the enormous potential and impact that stem cells may have on regenerative medicine, there has been a rapidly growing interest for tools to analyze and characterize the behaviors of these cells in vitro in an automated and high throughput fashion. Among these behaviors, mitosis, or cell division, is important since stem cells proliferate and renew themselves through mitosis. However, current automated systems for measuring cell proliferation often require destructive or sacrificial methods of cell manipulation such as cell lysis or in vitro staining. In this paper, we propose an effective approach for automated mitosis detection using phase-contrast time-lapse microscopy, which is a nondestructive imaging modality, thereby allowing continuous monitoring of cells in culture. In our approach, we present a probabilistic model for event detection, which can simultaneously 1) identify spatio-temporal patch sequences that contain a mitotic event and 2) localize a birth event, defined as the time and location at which cell division is completed and two daughter cells are born. Our approach significantly outperforms previous approaches in terms of both detection accuracy and computational efficiency, when applied to multipotent C3H10T1/2 mesenchymal and C2C12 myoblastic stem cell populations.. |
| 55. | Seungil Huh, Dai Fei Elmer Ker, Ryoma Bise, Mei Chen, Takeo Kanade, Automated mitosis detection of stem cell populations in phase-contrast microscopy images, IEEE Transactions on Medical Imaging, 10.1109/TMI.2010.2089384, 30, 3, 586-596, 2011.03, Due to the enormous potential and impact that stem cells may have on regenerative medicine, there has been a rapidly growing interest for tools to analyze and characterize the behaviors of these cells in vitro in an automated and high throughput fashion. Among these behaviors, mitosis, or cell division, is important since stem cells proliferate and renew themselves through mitosis. However, current automated systems for measuring cell proliferation often require destructive or sacrificial methods of cell manipulation such as cell lysis or in vitro staining. In this paper, we propose an effective approach for automated mitosis detection using phase-contrast time-lapse microscopy, which is a nondestructive imaging modality, thereby allowing continuous monitoring of cells in culture. In our approach, we present a probabilistic model for event detection, which can simultaneously 1) identify spatio-temporal patch sequences that contain a mitotic event and 2) localize a birth event, defined as the time and location at which cell division is completed and two daughter cells are born. Our approach significantly outperforms previous approaches in terms of both detection accuracy and computational efficiency, when applied to multipotent C3H10T1/2 mesenchymal and C2C12 myoblastic stem cell populations.. |
| 56. | Takeo Kanade, Zhaozheng Yin, Ryoma Bise, Seungil Huh, Sungeun Eom, Michael F. Sandbothe, Mei Chen, Cell image analysis Algorithms, system and applications, 2011 IEEE Workshop on Applications of Computer Vision, WACV 2011, 10.1109/WACV.2011.5711528, 374-381, 2011.03, We present several algorithms for cell image analysis including microscopy image restoration, cell event detection and cell tracking in a large population. The algorithms are integrated into an automated system capable of quantifying cell proliferation metrics in vitro in real-time. This offers unique opportunities for biological applications such as efficient cell behavior discovery in response to different cell culturing conditions and adaptive experiment control. We quantitatively evaluated our system's performance on 16 microscopy image sequences with satisfactory accuracy for biologists' need. We have also developed a public website compatible to the system's local user interface, thereby allowing biologists to conveniently check their experiment progress online. The website will serve as a community resource that allows other research groups to upload their cell images for analysis and comparison.. |
| 57. | Sungeun Eom, Ryoma Bise, Takeo Kanade, Detection of hematopoietic stem cells in microscopy images using a bank of ring filters, 7th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2010, 10.1109/ISBI.2010.5490394, 137-140, 2010.08, We present a method for robustly detecting hematopoietic stem cells (HSCs) in phase contrast microscopy images. HSCs appear to be easy to detect since they typically appear as round objects. However, when HSCs are touching and overlapping, showing the variations in shape and appearance, standard pattern detection methods, such as Hough transform and correlation, do not perform well. The proposed method exploits the output pattern of a ring filter bank applied to the input image, which consists of a series of matched filters with multiple-radius ring-shaped templates. By modeling the profile of each filter response as a quadratic surface, we explore the variations of peak curvatures and peak values of the filter responses when the ring radius varies. The method is validated on thousands of phase contrast microscopy images with different acquisition settings, achieving 96.5% precision and 94.4% recall.. |
| 58. | Zhaozheng Yin, Ryoma Bise, Mei Chen, Takeo Kanade, Cell segmentation in microscopy imagery using a bag of local Bayesian classifiers, 7th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2010, 10.1109/ISBI.2010.5490399, 125-128, 2010.01, Cell segmentation in microscopy imagery is essential for many bioimage applications such as cell tracking. To segment cells from the background accurately, we present a pixel classification approach that is independent of cell type or imaging modality. We train a set of Bayesian classifiers from clustered local training image patches. Each Bayesian classifier is an expert to make decision in its specific domain. The decision from the mixture of experts determines how likely a new pixel is a cell pixel. We demonstrate the effectiveness of this approach on four cell types with diverse morphologies under different microscopy imaging modalities.. |
| 59. | R. Bise, K. Li, S. Eom, and T. Kanade, Reliably Tracking Partially Overlapping Neural Stem Cells in DIC Microscopy Image Sequences , MICCAI Workshop on OPTMHisE, 2009.10. |
| 60. | Ryoma Bise, Norikazu Takahashi, Tetsuo Nishi, An improvement of the design method of cellular neural networks based on generalized eigenvalue minimization, IEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications, 10.1109/TCSI.2003.819827, 50, 12, 1569-1574, 2003.12, Realization of associative memories by cellular neural networks (CNNs) with binary output is studied. Concerning this problem, a CNN design method based upon generalized eigenvalue minimization (GEVM) has recently been proposed. In this brief, a new CNN design method which is based on the GEVM-based method will be presented. We first give some analytical results related to the basin of attraction of a memory vector. We then derive the design method by combining these analytical results and the GEVM-based method. We finally show through computer simulations that the proposed method can achieve higher recall probability than the original GEVM-based method.. |
| 61. | Ryoma Bise, Norikazu Takahashi, Tetsuo Nishi, An improvement of the design method of cellular neural networks based on generalized eigenvalue minimization, IEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications, 10.1109/TCSI.2003.819827, 50, 12, 1569-1574, 2003.12, Realization of associative memories by cellular neural networks (CNNs) with binary output is studied. Concerning this problem, a CNN design method based upon generalized eigenvalue minimization (GEVM) has recently been proposed. In this brief, a new CNN design method which is based on the GEVM-based method will be presented. We first give some analytical results related to the basin of attraction of a memory vector. We then derive the design method by combining these analytical results and the GEVM-based method. We finally show through computer simulations that the proposed method can achieve higher recall probability than the original GEVM-based method.. |
| 62. | R. Bise, N. Takahashi, and T. Nishi, On the design method of cellular neural networks for associative memories based on generalized eigenvalue problem, IEEE Cellular Neural Networks and Their Applications, 2002.08. |
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