Kyushu University Academic Staff Educational and Research Activities Database
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Shoko Miyauchi Last modified date:2023.09.29

Graduate School
Undergraduate School

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 Reseacher Profiling Tool Kyushu University Pure
Academic Degree
Ph.D (Eng.)
Country of degree conferring institution (Overseas)
Field of Specialization
Medical Image Processing
Total Priod of education and research career in the foreign country
Research Interests
  • Construction of Denoising & Mesher DNN for 3D object recognition
    keyword : 3D object recognition, Deep learning
  • Construction of Multi-dimensional Statistical Shape Model for a heart
    keyword : Statistical Shape Model
  • Mapping of human organ models onto target spaces for the systematization of human organs
    keyword : Medical images, Surface model, Volumetric model, Statistical shape model
  • Innovative Computer-Aided Cancer Diagnosis with Artificial Intelligence using Cell Shape and Appearance
    keyword : cancer diagnosis, cell classification
Academic Activities
1. Kurazume, Tomoki Hiramatsu, Masaya Kamei, Daiji Inoue, Akihiro Kawamura, Shoko Miyauchi, and Qi An, Development of AR training systems for Humanitude dementia care, 10.1080/01691864.2021.2017342, 2022.01.
2. Junichi Inokuchi, Fumio Kinoshita, Yoshinao Oda, Masatoshi Eto, Ryo Kurazume, Ken'ichi Morooka, Jun Mutaguchi, Satoshi Kobayashi, Shoko Miyauchi, Aiko Umehara, Artificial intelligence for segmentation of bladder tumor cystoscopic images performed by U-Net with dilated convolution, 10.1089/end.2021.0483, 2022.01.
3. Fumiaki Ichihashi, Akira Koyama, Tetsuya Akashi, Shoko Miyauchi, Ken'ichi Morooka, Hajime Hojo, Hisahiro Einaga, Yoshio Takahashi, Toshiaki Tanigaki, Hiroyuki Shinada, Yasukazu Murakami, Automatic electron hologram acquisition of catalyst nanoparticles using particle detection with image processing and machine learning, Applied Physics Letters, 10.1063/5.0074231, 120, 6, 1-6, 2022.02, To enable better statistical analysis of catalyst nanoparticles by high-resolution electron holography, we improved the particle detection accuracy of our previously developed automated hologram acquisition system by using an image classifier trained with machine learning. The detection accuracy of 83% was achieved with the small training data of just 232 images showing nanoparticles by utilizing transfer learning based on VGG16 to train the image classifier. Although the construction of training data generally requires much effort, the time needed to select the training data candidates was significantly shortened by utilizing a pattern matching technique. Experimental results showed that the high-resolution hologram acquisition efficiency was improved by factors of about 100 and 6 compared to a scan method and a pattern-matching-only method, respectively..
4. Ken’ichi Morooka, Ryota Matsubara, Shoko Miyauchi, Takaichi Fukuda, Takeshi Sugii, Ryo Kurazume, Ancient pelvis reconstruction from collapsed component bones using statistical shape models, Machine Vision and Applications, 59-69, 2019.02.
5. Shoko Miyauchi, Ken'ichi Morooka, Tokuo Tsuji, Yasushi Miyagi, Takaichi Fukuda, Ryo Kurazume, Fast modified Self-organizing Deformable Model: Geometrical feature-preserving mapping of organ models onto target surfaces with various shapes and topologies, Computer Methods and Programs in Biomedicine, 157, 237-250, 2018.01.
1. Shoko Miyauchi, Ken'ichi Morooka, Tokuo Tsuji, Yasushi Miyagi, Takaichi Fukuda, Ryo Kurazume, Angle- and Volume-Preserving Mapping of Organ Volume Model Based on modified Self-organizing Deformable Model, 23rd International Conference on Pattern Recognition (ICPR 2016), 2016.12.
Membership in Academic Society
  • IEEE
Other Educational Activities
  • 2019.09.