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9 results on '"Inubushi, Tomoo"'

1. Neural substrates of cough control during coughing

Author

Sugi, Takafumi, Inubushi, Tomoo, Ohno, Tomohisa, Onishi, Yuya, Isobe, Takashi, Shigematsu, Takashi, Hanai, Satoshi, Okada, Yoshiro, Takahashi, Ryosuke, Tawara, Yuichi, Suzuki, Chie, Kanno, Toshihiko, Magata, Yasuhiro, Fujishima, Ichiro, Yoshikawa, Etsuji, and Ouchi, Yasuomi

Published
2024
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2. Biomedical image analysis competitions: The state of current participation practice

Author

Eisenmann, Matthias, Reinke, Annika, Weru, Vivienn, Tizabi, Minu Dietlinde, Isensee, Fabian, Adler, Tim J., Godau, Patrick, Cheplygina, Veronika, Kozubek, Michal, Ali, Sharib, Gupta, Anubha, Kybic, Jan, Noble, Alison, de Solórzano, Carlos Ortiz, Pachade, Samiksha, Petitjean, Caroline, Sage, Daniel, Wei, Donglai, Wilden, Elizabeth, Alapatt, Deepak, Andrearczyk, Vincent, Baid, Ujjwal, Bakas, Spyridon, Balu, Niranjan, Bano, Sophia, Bawa, Vivek Singh, Bernal, Jorge, Bodenstedt, Sebastian, Casella, Alessandro, Choi, Jinwook, Commowick, Olivier, Daum, Marie, Depeursinge, Adrien, Dorent, Reuben, Egger, Jan, Eichhorn, Hannah, Engelhardt, Sandy, Ganz, Melanie, Girard, Gabriel, Hansen, Lasse, Heinrich, Mattias, Heller, Nicholas, Hering, Alessa, Huaulmé, Arnaud, Kim, Hyunjeong, Landman, Bennett, Li, Hongwei Bran, Li, Jianning, Ma, Jun, Martel, Anne, Martín-Isla, Carlos, Menze, Bjoern, Nwoye, Chinedu Innocent, Oreiller, Valentin, Padoy, Nicolas, Pati, Sarthak, Payette, Kelly, Sudre, Carole, van Wijnen, Kimberlin, Vardazaryan, Armine, Vercauteren, Tom, Wagner, Martin, Wang, Chuanbo, Yap, Moi Hoon, Yu, Zeyun, Yuan, Chun, Zenk, Maximilian, Zia, Aneeq, Zimmerer, David, Bao, Rina, Choi, Chanyeol, Cohen, Andrew, Dzyubachyk, Oleh, Galdran, Adrian, Gan, Tianyuan, Guo, Tianqi, Gupta, Pradyumna, Haithami, Mahmood, Ho, Edward, Jang, Ikbeom, Li, Zhili, Luo, Zhengbo, Lux, Filip, Makrogiannis, Sokratis, Müller, Dominik, Oh, Young-tack, Pang, Subeen, Pape, Constantin, Polat, Gorkem, Reed, Charlotte Rosalie, Ryu, Kanghyun, Scherr, Tim, Thambawita, Vajira, Wang, Haoyu, Wang, Xinliang, Xu, Kele, Yeh, Hung, Yeo, Doyeob, Yuan, Yixuan, Zeng, Yan, Zhao, Xin, Abbing, Julian, Adam, Jannes, Adluru, Nagesh, Agethen, Niklas, Ahmed, Salman, Khalil, Yasmina Al, Alenyà, Mireia, Alhoniemi, Esa, An, Chengyang, Anwar, Talha, Arega, Tewodros Weldebirhan, Avisdris, Netanell, Aydogan, Dogu Baran, Bai, Yingbin, Calisto, Maria Baldeon, Basaran, Berke Doga, Beetz, Marcel, Bian, Cheng, Bian, Hao, Blansit, Kevin, Bloch, Louise, Bohnsack, Robert, Bosticardo, Sara, Breen, Jack, Brudfors, Mikael, Brüngel, Raphael, Cabezas, Mariano, Cacciola, Alberto, Chen, Zhiwei, Chen, Yucong, Chen, Daniel Tianming, Cho, Minjeong, Choi, Min-Kook, Xie, Chuantao Xie Chuantao, Cobzas, Dana, Cohen-Adad, Julien, Acero, Jorge Corral, Das, Sujit Kumar, de Oliveira, Marcela, Deng, Hanqiu, Dong, Guiming, Doorenbos, Lars, Efird, Cory, Escalera, Sergio, Fan, Di, Serj, Mehdi Fatan, Fenneteau, Alexandre, Fidon, Lucas, Filipiak, Patryk, Finzel, René, Freitas, Nuno R., Friedrich, Christoph M., Fulton, Mitchell, Gaida, Finn, Galati, Francesco, Galazis, Christoforos, Gan, Chang Hee, Gao, Zheyao, Gao, Shengbo, Gazda, Matej, Gerats, Beerend, Getty, Neil, Gibicar, Adam, Gifford, Ryan, Gohil, Sajan, Grammatikopoulou, Maria, Grzech, Daniel, Güley, Orhun, Günnemann, Timo, Guo, Chunxu, Guy, Sylvain, Ha, Heonjin, Han, Luyi, Han, Il Song, Hatamizadeh, Ali, He, Tian, Heo, Jimin, Hitziger, Sebastian, Hong, SeulGi, Hong, SeungBum, Huang, Rian, Huang, Ziyan, Huellebrand, Markus, Huschauer, Stephan, Hussain, Mustaffa, Inubushi, Tomoo, Polat, Ece Isik, Jafaritadi, Mojtaba, Jeong, SeongHun, Jian, Bailiang, Jiang, Yuanhong, Jiang, Zhifan, Jin, Yueming, Joshi, Smriti, Kadkhodamohammadi, Abdolrahim, Kamraoui, Reda Abdellah, Kang, Inha, Kang, Junghwa, Karimi, Davood, Khademi, April, Khan, Muhammad Irfan, Khan, Suleiman A., Khantwal, Rishab, Kim, Kwang-Ju, Kline, Timothy, Kondo, Satoshi, Kontio, Elina, Krenzer, Adrian, Kroviakov, Artem, Kuijf, Hugo, Kumar, Satyadwyoom, La Rosa, Francesco, Lad, Abhi, Lee, Doohee, Lee, Minho, Lena, Chiara, Li, Hao, Li, Ling, Li, Xingyu, Liao, Fuyuan, Liao, KuanLun, Oliveira, Arlindo Limede, Lin, Chaonan, Lin, Shan, Linardos, Akis, Linguraru, Marius George, Liu, Han, Liu, Tao, Liu, Di, Liu, Yanling, Lourenço-Silva, João, Lu, Jingpei, Lu, Jiangshan, Luengo, Imanol, Lund, Christina B., Luu, Huan Minh, Lv, Yi, Macar, Uzay, Maechler, Leon, L., Sina Mansour, Marshall, Kenji, Mazher, Moona, McKinley, Richard, Medela, Alfonso, Meissen, Felix, Meng, Mingyuan, Miller, Dylan, Mirjahanmardi, Seyed Hossein, Mishra, Arnab, Mitha, Samir, Mohy-ud-Din, Hassan, Mok, Tony Chi Wing, Murugesan, Gowtham Krishnan, Karthik, Enamundram Naga, Nalawade, Sahil, Nalepa, Jakub, Naser, Mohamed, Nateghi, Ramin, Naveed, Hammad, Nguyen, Quang-Minh, Quoc, Cuong Nguyen, Nichyporuk, Brennan, Oliveira, Bruno, Owen, David, Pal, Jimut Bahan, Pan, Junwen, Pan, Wentao, Pang, Winnie, Park, Bogyu, Pawar, Vivek, Pawar, Kamlesh, Peven, Michael, Philipp, Lena, Pieciak, Tomasz, Plotka, Szymon, Plutat, Marcel, Pourakpour, Fattaneh, Preložnik, Domen, Punithakumar, Kumaradevan, Qayyum, Abdul, Queirós, Sandro, Rahmim, Arman, Razavi, Salar, Ren, Jintao, Rezaei, Mina, Rico, Jonathan Adam, Rieu, ZunHyan, Rink, Markus, Roth, Johannes, Ruiz-Gonzalez, Yusely, Saeed, Numan, Saha, Anindo, Salem, Mostafa, Sanchez-Matilla, Ricardo, Schilling, Kurt, Shao, Wei, Shen, Zhiqiang, Shi, Ruize, Shi, Pengcheng, Sobotka, Daniel, Soulier, Théodore, Fadida, Bella Specktor, Stoyanov, Danail, Mun, Timothy Sum Hon, Sun, Xiaowu, Tao, Rong, Thaler, Franz, Théberge, Antoine, Thielke, Felix, Torres, Helena, Wahid, Kareem A., Wang, Jiacheng, Wang, YiFei, Wang, Wei, Wang, Xiong, Wen, Jianhui, Wen, Ning, Wodzinski, Marek, Wu, Ye, Xia, Fangfang, Xiang, Tianqi, Xiaofei, Chen, Xu, Lizhan, Xue, Tingting, Yang, Yuxuan, Yang, Lin, Yao, Kai, Yao, Huifeng, Yazdani, Amirsaeed, Yip, Michael, Yoo, Hwanseung, Yousefirizi, Fereshteh, Yu, Shunkai, Yu, Lei, Zamora, Jonathan, Zeineldin, Ramy Ashraf, Zeng, Dewen, Zhang, Jianpeng, Zhang, Bokai, Zhang, Jiapeng, Zhang, Fan, Zhang, Huahong, Zhao, Zhongchen, Zhao, Zixuan, Zhao, Jiachen, Zhao, Can, Zheng, Qingshuo, Zhi, Yuheng, Zhou, Ziqi, Zou, Baosheng, Maier-Hein, Klaus, Jäger, Paul F., Kopp-Schneider, Annette, and Maier-Hein, Lena

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known about the common practice as well as bottlenecks faced by the community in tackling the research questions posed. To shed light on the status quo of algorithm development in the specific field of biomedical imaging analysis, we designed an international survey that was issued to all participants of challenges conducted in conjunction with the IEEE ISBI 2021 and MICCAI 2021 conferences (80 competitions in total). The survey covered participants' expertise and working environments, their chosen strategies, as well as algorithm characteristics. A median of 72% challenge participants took part in the survey. According to our results, knowledge exchange was the primary incentive (70%) for participation, while the reception of prize money played only a minor role (16%). While a median of 80 working hours was spent on method development, a large portion of participants stated that they did not have enough time for method development (32%). 25% perceived the infrastructure to be a bottleneck. Overall, 94% of all solutions were deep learning-based. Of these, 84% were based on standard architectures. 43% of the respondents reported that the data samples (e.g., images) were too large to be processed at once. This was most commonly addressed by patch-based training (69%), downsampling (37%), and solving 3D analysis tasks as a series of 2D tasks. K-fold cross-validation on the training set was performed by only 37% of the participants and only 50% of the participants performed ensembling based on multiple identical models (61%) or heterogeneous models (39%). 48% of the respondents applied postprocessing steps.

Published
2022

4. Kinetics-Induced Block Matching and 5-D Transform Domain Filtering for Dynamic PET Image Denoising

Author

Takashi Isobe, Takahiro Moriya, Ryosuke Ota, A. Tokui, Etsuji Yoshikawa, Akihiro Kakimoto, Kibo Ote, Fumio Hashimoto, A. Saito, Tomohide Omura, Atsushi Teramoto, Yasuomi Ouchi, and Inubushi Tomoo

Subjects
Computer science, Image quality, business.industry, Noise reduction, Wiener filter, Pattern recognition, Peak signal-to-noise ratio, Thresholding, Atomic and Molecular Physics, and Optics, Gaussian filter, symbols.namesake, symbols, Radiology, Nuclear Medicine and imaging, Bilateral filter, Artificial intelligence, business, Instrumentation, Image restoration
Abstract

Dynamic positron emission tomography (PET) scans of short-time frames are required to quantitatively estimate the uptake of PET ligands. Because such short-frame scans tend to be noisy, we propose kinetics-induced block matching and 5-D transform domain filtering (KIBM5D) specialized for dynamic PET image denoising. In the proposed algorithm, kinetics-induced block matching (KIBM) and 5-D transform domain filtering are alternately repeated in two cascading stages. In each stage of KIBM5D, all time frames are included in a patch of the KIBM to collect similar patch-wise time activity curves. These similar 4-D patches are then five-dimensionally grouped and transformed to the 5-D spectrum. In the 5-D transform domain, the 5-D spectrum is shrunk by hard thresholding and Wiener filtering in the first and second stage of KIBM5D, respectively. The sparsity of the 5-D spectrum is improved because signals of similar 4-D patches are correlated, while noises of these are uncorrelated. To evaluate the performance of KIBM5D, we used both computer simulation data of a dynamic digital brain phantom using [18F]FDG kinetics, and experimental data of a normal healthy volunteer using [11C]MeQAA, and compared the results of KIBM5D, Gaussian filter (GF), bilateral filter, nonlocal means, block matching and 4-D filtering, and 4-D Gaussian filtering. For simulation data, KIBM5D performed superiorly to the other methods in terms of the peak signal to noise ratio and structural similarity measures, in all time frames. Additionally, KIBM5D generated the best image quality not only in simulations but also with human data. Accordingly, KIBM5D enables the efficient denoising of dynamic PET images.

Published
2020
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5. Neural correlates of head restraint: Unsolicited neuronal activation and dopamine release

Author

Toshihiko Kanno, Yasuomi Ouchi, Kengo Sato, Tomomi Shinke, Masami Futatsubashi, Akihiro Kakimoto, Yutaro Mori, Masamichi Yokokura, Etsuji Yoshikawa, Yosuke Kameno, Inubushi Tomoo, Masanori Ito, Hiroyuki Okada, and Shigeru Ito

Subjects
Adult, Male, Restraint, Physical, Positron emission tomography, Cognitive Neuroscience, Dopamine, Anxiety, 050105 experimental psychology, lcsh:RC321-571, 03 medical and health sciences, Young Adult, 0302 clinical medicine, Neuroimaging, Oxygen Radioisotopes, Stress, Physiological, Dopamine release, Medicine, Humans, 0501 psychology and cognitive sciences, Carbon Radioisotopes, Head restraint, lcsh:Neurosciences. Biological psychiatry. Neuropsychiatry, Raclopride, business.industry, Working memory, Putamen, Functional Neuroimaging, Stress response, 05 social sciences, Brain, Cerebral blood flow, Healthy Volunteers, Memory, Short-Term, Neurology, Cerebrovascular Circulation, Positron-Emission Tomography, medicine.symptom, business, Neuroscience, Head, 030217 neurology & neurosurgery, Stress, Psychological, medicine.drug
Abstract

To minimize motion-related distortion of reconstructed images, conventional positron emission tomography (PET) measurements of the brain inevitably require a firm and tight head restraint. While such a restraint is now a routine procedure in brain imaging, the physiological and psychological consequences resulting from the restraint have not been elucidated. To address this problem, we developed a restraint-free brain PET system and conducted PET scans under both restrained and non-restrained conditions. We examined whether head restraint during PET scans could alter brain activities such as regional cerebral blood flow (rCBF) and dopamine release along with psychological stress related to head restraint. Under both conditions, 20 healthy male participants underwent [15O]H2O and [11C]Raclopride PET scans during working memory tasks with the same PET system. Before, during, and after each PET scan, we measured physiological and psychological stress responses, including the State-Trait Anxiety Inventory (STAI) scores. Analysis of the [15O]H2O-PET data revealed higher rCBF in regions such as the parahippocampus in the restrained condition. We found the binding potential (BPND) of [11C]Raclopride in the putamen was significantly reduced in the restrained condition, which reflects an increase in dopamine release. Moreover, the restraint-induced change in BPND was correlated with a shift in the state anxiety score of the STAI, indicating that less anxiety accompanied smaller dopamine release. These results suggest that the stress from head restraint could cause unsolicited responses in brain physiology and emotional states. The restraint-free imaging system may thus be a key enabling technology for the natural depiction of the mind.

Published
2021

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