Current Affiliation Organization 【 display / non-display 】

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Affiliated academic organizations 【 display / non-display 】

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Research Areas 【 display / non-display 】

Published Papers 【 display / non-display 】

Published Papers 【 display / non-display 】

• Simulation of time-intensity curve based on k-space filling in breast dynamic contrast-enhanced three-dimensional magnetic resonance imaging.

  Yasuo Takatsu, Tsuyoshi Ueyama, Takahiro Iwasaki, Tomoko Tateyama, Tosiaki Miyati

  Radiological physics and technology   17 ( 2 ) 536 - 552   2024.06 [ Peer Review Accepted ]

  Type of publication: Research paper (scientific journal)

  　View Summary

  This study elucidated the effects of a three-dimensional k-space trajectory incorporating the partial Fourier (PF) technique on a time-intensity curve (TIC) in a dynamic contrast-enhanced magnetic resonance imaging of a typical malignant breast tumor using a digital phantom. Images were obtained from the Cancer Imaging Archive Open Data for Breast Cancer, and 1-min scans with high temporal resolution were analyzed. The order of the k-space trajectory was set as Linear (sequential), Low-High (centric), PF (62.5%; Z-, Y-, and both directions), and Low-High Radial. k0 (center of the k-space) timing and TIC shape were affected by the chosen k-space trajectory and implementation of the PF technique. A small TIC gradient was obtained using a Low-High Radial order. If the k-space filling method (particularly the radial method) produces a gentle TIC gradient, misinterpretation could arise during the assessment of tumor malignancy status.

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• A motion-aware and temporal-enhanced Spatial–Temporal Graph Convolutional Network for skeleton-based human action segmentation

  Shurong Chai, Rahul Kumar Jain, Jiaqing Liu, Shiyu Teng, Tomoko Tateyama, Yinhao Li, Yen-Wei Chen

  Neurocomputing ( Elsevier BV )  580   127482 - 127482   2024.05 [ Peer Review Accepted ]

  Type of publication: Research paper (scientific journal)

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• An Intra- and Inter-Emotion Transformer-Based Fusion Model with Homogeneous and Diverse Constraints Using Multi-Emotional Audiovisual Features for Depression Detection

  Shiyu TENG, Jiaqing LIU, Yue HUANG, Shurong CHAI, Tomoko TATEYAMA, Xinyin HUANG, Lanfen LIN, Yen-Wei CHEN

  IEICE Transactions on Information and Systems ( Institute of Electronics, Information and Communications Engineers (IEICE) )  E107.D ( 3 ) 342 - 353   2024.03 [ Peer Review Accepted ]

  Type of publication: Research paper (scientific journal)

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• Usefulness of fat-containing agents: an initial study on estimating fat content for magnetic resonance imaging.

  Yasuo Takatsu, Hiroshi Ohnishi, Tomoko Tateyama, Tosiaki Miyati

  Physical and engineering sciences in medicine   47 ( 1 ) 339 - 350   2024.03 [ Peer Review Accepted ]

  Type of publication: Research paper (scientific journal)

  　View Summary

  This initial study aimed at testing whether fat-containing agents can be used for the fat mass estimation methods using magnetic resonance imaging (MRI). As an example for clinical application, fat-containing agents (based on soybean oil, 10% and 20%), 100% soybean oil, and saline as reference substances were placed outside the proximal femurs obtained from 14 participants and analyzed by 0.3 T MRI. Fat content was the estimated fat fraction (FF) based on signal intensity (SIeFF, %). The SIeFF values of the femoral bone marrow, including the femoral head, neck, shaft, and trochanter area, were measured. MRI data were compared in terms of bone mineral content (BMC) and bone mineral density (BMD) by dual-energy X-ray absorptiometry (DXA) in the proximal femur. Twelve pig femurs were also used to confirm the correlation between FF by the DIXON method and SIeFF. According to Pearson's correlation coefficient, the SIeFF and total BMC and BMD data revealed strong and moderate negative correlations in the femoral head (r <  - 0.74) and other sites (r =  - 0.66 to - 0.45). FF and SIeFF showed a strong correlation (r = 0.96). This study was an initial investigation of a method for estimating fat mass with fat-containing agents and showed the potential for use in MRI. SIeFF and FF showed a strong correlation, and SIeFF and BMD and BMC showed correlation; however, further studies are needed to use SIeFF as a substitute for DXA.

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• Multi-Modal and Multi-Task Depression Detection with Sentiment Assistance.

  Shiyu Teng, Shurong Chai, Jiaqing Liu, Tomoko Tateyama, Lanfen Lin, Yen-Wei Chen 0001

  ICCE     1 - 5   2024 [ Peer Review Accepted ]

  Type of publication: Research paper (international conference proceedings)

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Other Papers 【 display / non-display 】

Other Papers 【 display / non-display 】

• Examination of high accuracy of mango grade classification by image analysis and composition of fruit imaging environment

  城間康, 健山智子, 平良英三, 長山格

  電気学会研究会資料   ( IIS-21-001-004.006 ) 13 - 14   2021.02

   

  J-GLOBAL

• Student Understanding Evaluation from Descriptive Responses based on Principal Component analysis

  森田博人, 健山智子, 松本慎平

  教育システム情報学会全国大会講演論文集(CD-ROM)   46th   172 - 174   2021.02

   

  J-GLOBAL

• 3D Human Body Pose Estimation Using Graph Convolutional Networks

  木下将児, LIU Jiaqing, 健山智子, 岩本祐太郎, CHEN Yen-Wei

  映像情報メディア学会技術報告   45 ( 7 ) 27 - 28   2021.02

   

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• A CNN-Transformer-Based Network for Depression Recognition

  LIU Jia-Qing, CHAI Shu-Rong, HUANG Yue, HUANG Xin-Yin, 健山智子, 岩本祐太郎, CHEN Yen-Wei

  電子情報通信学会技術研究報告(Web)   120 ( 409 ) 83 - 85   2021.02

   

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• Lecture Keyword Detection and Evaluation of Learning Understandings Based on Text Analysis

  森田博人, 健山智子, 折本研, 松本慎平

  電気学会研究会資料   ( IS-20-034-046.048.051-066 ) 113 - 116   2020.10

   

  J-GLOBAL

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Grant-in-Aid for Scientific Research 【 display / non-display 】

Grant-in-Aid for Scientific Research 【 display / non-display 】

• Elucidation of the Paper Road by data science. -Based on Quantitative, Qualitative research and AI Multidimensional analysis-

  Grant-in-Aid for Scientific Research(A)

  Project Year: 2022.04  -  2027.03 

  Direct: 32,300,000 (YEN)  Overheads: 41,990,000 (YEN)  Total: 9,690,000 (YEN)

• Grant-in-Aid for Scientific Research(C)

  Project Year: 2020  -  2025.03 

  Direct: 3,200,000 (YEN)  Overheads: 4,160,000 (YEN)  Total: 960,000 (YEN)

• Gesture model and database construct for intaractive visualization of medical images in surgery

  Grant-in-Aid for Scientific Research(C)

  Project Year: 2018.04  -  2021.03 

  Investigator(s): Tateyama Tomoko 

  Direct: 3,400,000 (YEN)  Overheads: 4,420,000 (YEN)  Total: 1,020,000 (YEN)

  　View Summary

  In this study, we developed the touch-less interactive system for visualization medical images based on gesture recognition using RGB-D sensors in an simulated surgery environment. The gestures constructed in this study consisted of 25 different gesture types, which were observed from the front and from the upper 45-degree frontal direction, and which were simultaneously acquired in terms of color and depth information. Then, the dataset was published as the MaHG-RGBD database. In addition, for more efficient and accurate gesture analysis, we adopted deep learning to perform more detailed feature analysis and recognition.