Updated on 2024/10/03

写真a

 
TSUTSUMI Masato
 
Organization
Graduate School of Medicine Center for Neurological Diseases and Cance Designated assistant professor
Title
Designated assistant professor

Research Interests 1

  1. 形態 機械学習 定量生物学

Research Areas 1

  1. Informatics / Life, health and medical informatics  / 定量生物学

Research History 2

  1. Hiroshima University

    2023.10 - 2024.9

  2. RIKEN

    2020.4 - 2023.3

Education 2

  1. The University of Tokyo   Graduate School of Science Doctoral Program

    2020.4 - 2023.9

  2. The University of Tokyo   Graduate School of Science

    2018.4 - 2020.3

 

Papers 2

  1. Deciphering the origin of developmental stability: The role of intracellular expression variability in evolutionary conservation Reviewed

    Yui Uchida, Masato Tsutsumi, Shunsuke Ichii, Naoki Irie, Chikara Furusawa

    Evolution & Development   Vol. 26 ( e12473 )   2024.3

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    Language:English   Publishing type:Research paper (scientific journal)  

    DOI: 10.1111/ede.12473

    DOI: 10.1111/ede.12473

  2. A deep learning approach for morphological feature extraction based on variational auto-encoder: an application to mandible shape Reviewed

    Masato Tsutsumi, Nen Saito, Daisuke Koyabu, Chikara Furusawa

    npj Systems Biology and Applications   Vol. 9 ( 30 )   2023.7

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    Language:English   Publishing type:Research paper (scientific journal)  

    <jats:title>Abstract</jats:title><jats:p>Shape measurements are crucial for evolutionary and developmental biology; however, they present difficulties in the objective and automatic quantification of arbitrary shapes. Conventional approaches are based on anatomically prominent landmarks, which require manual annotations by experts. Here, we develop a machine-learning approach by presenting morphological regulated variational AutoEncoder (Morpho-VAE), an image-based deep learning framework, to conduct landmark-free shape analysis. The proposed architecture combines the unsupervised and supervised learning models to reduce dimensionality by focusing on morphological features that distinguish data with different labels. We applied the method to primate mandible image data. The extracted morphological features reflected the characteristics of the families to which the organisms belonged, despite the absence of correlation between the extracted morphological features and phylogenetic distance. Furthermore, we demonstrated the reconstruction of missing segments from incomplete images. The proposed method provides a flexible and promising tool for analyzing a wide variety of image data of biological shapes even those with missing segments.</jats:p>

    DOI: 10.1038/s41540-023-00293-6

    DOI: 10.1038/s41540-023-00293-6

Presentations 19

  1. 深層学習による生物形態定量解析手法の開発 Invited

    堤真人, 斉藤稔, 古澤力

    日本応用数理学会2024年度 年会  2024.9.16 

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    Event date: 2024.9

    Presentation type:Oral presentation (invited, special)  

  2. 深層学習による形態定量解析手法の提案

    堤 真人, 斉藤 稔, 古澤 力

    2024年度日本数理生物学会年会  2024.9.12 

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    Event date: 2024.9

    Presentation type:Poster presentation  

  3. 数理モデルを用いた社会的敗北ストレス下でのマウス行動の定量化

    堤真人, 東野伊織, 福井雅也, 本田直樹

    CPSY TOKYO 2024  2024.3.28 

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    Event date: 2024.3

    Presentation type:Poster presentation  

  4. 深層学習を用いた 生物形態の定量解析手法の開発 Invited

    堤真人

    シリーズオンラインセミナー「AIが切り開く進化生物学の未来」 第1回「画像認識AIで挑む! ⾏動・形態形質定量化のレベルアップ」  2024.3.9 

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    Event date: 2024.3

    Presentation type:Oral presentation (invited, special)  

  5. ベイズ推論を用いた脳内反芻思考の数理モデル

    堤 真人, 東野 伊織, 福井 雅也, 本田 直樹

    理論生物学スプリングスクール2024  2024.2.20 

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    Event date: 2024.2

    Presentation type:Poster presentation  

  6. 深層学習を用いた 生物形態の定量解析手法の開発 Invited

    第8回理論免疫学ワークショップ  2024.2.14 

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    Event date: 2024.2

    Presentation type:Oral presentation (invited, special)  

  7. 自由エネルギー原理を用いた脳内反芻思考の数理モデル

    定量生物学の会 第十一回年会  2024.1.6 

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    Event date: 2024.1

    Presentation type:Poster presentation  

  8. Unveiling the enigmatic Middle Devonian vertebrate, Palaeospondylus

    Tatsuya Hirasawa, Masato Tsutsumi, Shunsuke Ichii, Shigeru Kuratani

    The 3rd Asia Evo Conference Symposium: early evolution of vertebrates from evo-devo and paleontological perspectives  2023.12.16 

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    Event date: 2023.12

    Presentation type:Symposium, workshop panel (public)  

  9. Development of a quantitative analysis method for biological morphology using deep learning

    Masato Tsutsumi, Nen Saito, Daisuke Koyabu, Chikara Furusawa

    2023.8.8 

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    Event date: 2023.8

    Presentation type:Poster presentation  

  10. 変分オートエンコーダを用いた霊長目の下顎形態特徴量抽出

    堤真人, 斉藤稔, 小薮大輔, 古澤力

    定量生物学の会第十回年会  2022.12.15 

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    Event date: 2022.12

    Presentation type:Poster presentation  

  11. 機械学習を用いた様々な生物形態の定量化 Invited

    堤真人

    広島大学数理生命科学プログラムセミナー  2022.11.10 

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    Event date: 2022.11

    Presentation type:Oral presentation (invited, special)  

  12. A method for morphological feature extraction based on variational auto-encoder: an application to mandible shape

    2022.9.30 

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    Event date: 2022.9

    Presentation type:Poster presentation  

  13. A Deep learning approach for the shape analysis of the primates mandible

    Masato Tsutsumi, Nen Saito, Daisuke Koyabu, Chikara Furusawa

    2022.6.1 

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    Event date: 2022.6

    Presentation type:Poster presentation  

  14. 機械学習を用いた生物形態の定量化とその応用について 霊長目の下顎骨を対象として Invited

    堤真人

    基礎生物学研究所部門セミナー  2021.10 

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    Event date: 2021.10

    Presentation type:Oral presentation (invited, special)  

  15. 機械学習を用いて顎の形態を定量化する

    堤真人

    第61回生物物理若手の会夏の学校  2021.9.7 

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    Event date: 2021.9

    Presentation type:Poster presentation  

  16. 機械学習を用いた生物形態の定量化とその応用

    第58回生物物理学会  2020.9 

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    Event date: 2020.9

    Presentation type:Poster presentation  

  17. 2段階ネットワークを用いた2次元OCT画像の分類手法の検証

    竹村 昌彦, 堤 真人, 河合 宏紀, 袴田 和巳, 藤 秀義

    第二回 日本メディカルAI学会 学術集会  2020.1.31 

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    Event date: 2020.1 - 2020.2

    Presentation type:Oral presentation (general)  

  18. Characterization of biological morphology by using machine learning

    2020 

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    Event date: 2020

    Presentation type:Poster presentation  

  19. Characterization of biological morphology by using machine learning

    2020.10 

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    Presentation type:Poster presentation  

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Industrial property rights 1

  1. バッチ効果の補正システム、制御プログラム、バッチ効果の補正方法

    坂口峻太, 堤真人, 西健太郎, 本田直樹

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    Application no:特願2024-162597  Date applied:2024.9