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Fundamental Technologies for Integration of Multiscale Spatiotemporal Morphology in MCA

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Multidisciplinary Computational Anatomy

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Abstract

This chapter presents the achievements of multiscale spatiotemporal statistical models during the whole period of the multidisciplinary computational anatomy project. Shimizu et al. built spatiotemporal statistical models along a time axis of embryos and children, in which a modeling method to deal with a small sample of data was developed and smoothness constraints along a time axis were introduced into the spatiotemporal statistical model. Modelling of organs with nested and neighbouring constraints was also studied by their group. Multiscale models were constructed, in which Shimizu et al. presented super-resolution techniques by dictionary learning and deep learning, and Shouno et al. studied super-resolution under a noisy environment in order to solve mapping problems among multimodal images. Kobayashi et al. developed algorithms for image understanding of microscopic images of a KPC mouse, where 3D tissues structures were recognised from pancreatic serial section images and from hyperspectral images.

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Acknowledgements

Studies of Subsection 4.2 were approved by the ethics committee of the Graduate School and Faculty of Medicine of Kyoto University (R0316, R0989) and the ethics committee of Tokyo University of Agriculture and Technology (No. 30-28). Study of a part of Subsection 4.2.2 was approved by the ethics committee at Children’s National Medical Center (approval no. 00003792) and the ethics committee of Tokyo University of Agriculture and Technology (No. 30-31). Study of a part of Subsection 4.3 was approved by the ethics committee at Fukui University (approval no. 20100064) and the ethics committee of Tokyo University of Agriculture and Technology (No. 30-27).

The studies in Sect. 4.2 were collaboration researches with Prof. Hontani (Nagoya Institute of Technology), Prof. Matsuzoe (Nagoya Institute of Technology), Prof. Yamada (Kyoto University), Prof. Takakuwa (Kyoto University) and Dr. Linguraru (Children’s National Health System). The micro-CT volumes resected from a KPC mouse of pancreatic cancer in Sections 4.3 and 4.4 was provided by Prof. Hashizume (Kyushu University), Prof. Ouchida, Dr. Iwamoto (Kyushu University) and Prof. Mori (Nagoya University). The studies in Sect. 4.3.2 were collaboration researches with Prof. Kido (Yamaguchi University) and Prof. Inai (Fukui University). The authors would like to thank all these people for valuable data, discussions and comments.

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Authors and Affiliations

  1. Institute of Engineering, Tokyo University of Agriculture and Technology, Tokyo, Japan

    Akinobu Shimizu

  2. Faculty of Health and Medical Care, Saitama Medical University, Saitama, Japan

    Naoki Kobayashi

  3. Graduate School of Information Engineering, The University of Electro-Communications, Tokyo, Japan

    Hayaru Shouno

Authors
  1. Akinobu Shimizu
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  2. Naoki Kobayashi
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  3. Hayaru Shouno
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Corresponding author

Correspondence to Akinobu Shimizu .

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Editors and Affiliations

  1. Professor Emeritus, Kyushu University, Fukuoka, Japan

    Makoto Hashizume

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Shimizu, A., Kobayashi, N., Shouno, H. (2022). Fundamental Technologies for Integration of Multiscale Spatiotemporal Morphology in MCA. In: Hashizume, M. (eds) Multidisciplinary Computational Anatomy. Springer, Singapore. https://doi.org/10.1007/978-981-16-4325-5_4

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  • DOI: https://doi.org/10.1007/978-981-16-4325-5_4

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