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Realization of Multidisciplinary Understanding of Skeletal Muscle by Constructing Deep Learning and Model Integration Theory

Research Project

Project/Area Number 21K12731
Research Category

Grant-in-Aid for Scientific Research (C)

Allocation TypeMulti-year Fund
Section一般
Review Section Basic Section 90130:Medical systems-related
Research InstitutionAichi Prefectural University

Principal Investigator

Kamiya Naoki  愛知県立大学, 情報科学部, 准教授 (00580945)

Project Period (FY) 2021-04-01 – 2024-03-31
Project Status Completed (Fiscal Year 2023)
Budget Amount *help
¥4,160,000 (Direct Cost: ¥3,200,000、Indirect Cost: ¥960,000)
Fiscal Year 2023: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2022: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
Fiscal Year 2021: ¥2,080,000 (Direct Cost: ¥1,600,000、Indirect Cost: ¥480,000)
Keywords骨格筋 / 脊柱起立筋 / L3断面 / 体組成 / 体腔 / 深層学習 / セグメンテーション / 表層筋 / 胸鎖乳突筋 / 筋認識 / モデル
Outline of Research at the Start

骨格筋はCT,MRIなど断層画像の多くで描出されるが,通常は対象疾患や周辺組織の読影に注力され,健康時より管理可能であるが,筋量を含む正確な状態把握は困難な課題である.我々は,モデルベースの骨格筋部位別認識技術,さらに深層学習を用いた骨格筋の部位別認識技術を有しているが,双方に利点と課題が明らかとなっている.特に,一般の画像処理タスクとは異なり,深層学習用の骨格筋のアノテーション作業は現実的ではない.本研究では,深層学習とモデル併用により,筋線維のミクロ・マクロ構造に着目した,AI時代の筋の記述法を提案し,全身表層筋の認識を実現することをゴールとする.

Outline of Final Research Achievements

In this research project, we aimed to develop a skeletal muscle segmentation technology that overcomes the challenges of whole-body skeletal muscle recognition by employing deep learning and model integration theory, as opposed to conventional single-element skeletal muscle recognition and analysis techniques based on skeletal muscle recognition. As a baseline, we used the automatic recognition of 8 relatively shape-specific skeletal muscles, which were previously recognized by model-based descriptions of muscle contours. We then developed a deep learning and model-based recognition technology that can be expanded to whole-body skeletal muscle recognition, compatible with annotation costs. This includes a recognition method for region-specific skeletal muscles using the erector spinae and body cavity as keys, as well as a real-quantity measurement technology for whole-body body composition, including skeletal muscle, rather than mere estimation.

Academic Significance and Societal Importance of the Research Achievements

本研究は,骨格筋の自動認識と解析を新しい視点で再定義することを目的として実施した.骨格筋の解析は医師による半自動セグメンテーションが主流であり,主に横断面積に焦点を当てているが,我々は深層学習ベースの骨格筋認識と形状・筋束モデルの併用による,全身規模による認識を目指した.本研究により,深層学習とモデルを併用することで,従来の単一要素による解析にとどまらず,筋のミクロ・マクロ構造の多角的な解析の糸口を構築できた.本研究は,萎縮性筋疾患の画像鑑別など新たな医学的見地を得るために必要な要素技術の一つとしての可能性があり,医学的,工学的,産業的にも意義を持つ.

Report

(4 results)
  • 2023 Annual Research Report   Final Research Report ( PDF )
  • 2022 Research-status Report
  • 2021 Research-status Report
  • Research Products

    (19 results)

All 2024 2023 2022 2021 Other

All Journal Article (4 results) (of which Peer Reviewed: 2 results,  Open Access: 2 results) Presentation (11 results) (of which Int'l Joint Research: 3 results) Book (1 results) Remarks (3 results)

  • [Journal Article] Simultaneous Learning of Erector Spinae Muscles for Automatic Segmentation of Site-Specific Skeletal Muscles in Body CT Images2024

    • Author(s)
      Kawamoto Masahiro、Kamiya Naoki、Zhou Xiangrong、Kato Hiroki、Hara Takeshi、Fujita Hiroshi
    • Journal Title

      IEEE Access

      Volume: 12 Pages: 15468-15476

    • DOI

      10.1109/access.2023.3335948

    • Related Report
      2023 Annual Research Report
    • Peer Reviewed / Open Access
  • [Journal Article] Automated segmentation of oblique abdominal muscle based on body cavity segmentation in torso CT images using U-Net2022

    • Author(s)
      Kamiya Naoki、Zhou Xiangrong、Kato Hiroki、Hara Takeshi、Fujita Hiroshi
    • Journal Title

      Proceedings of International Workshop on Advanced Imaging Technology 2022

      Volume: 12177 Pages: 12-12

    • DOI

      10.1117/12.2624316

    • Related Report
      2021 Research-status Report
  • [Journal Article] Automatic Segmentation of Supraspinatus Muscle via Bone-Based Localization in Torso Computed Tomography Images Using U-Net2021

    • Author(s)
      Wakamatsu Yuichi、Kamiya Naoki、Zhou Xiangrong、Kato Hiroki、Hara Takeshi、Fujita Hiroshi
    • Journal Title

      IEEE Access

      Volume: 9 Pages: 155555-155563

    • DOI

      10.1109/access.2021.3127565

    • Related Report
      2021 Research-status Report
    • Peer Reviewed / Open Access
  • [Journal Article] Relationship between number of annotations and accuracy in segmentation of the erector spinae muscle using Bayesian U-Net in torso CT images2021

    • Author(s)
      Wakamatsu Yuichi、Kamiya Naoki、Zhou Xiangrong、Hara Takeshi、Fujita Hiroshi
    • Journal Title

      Proceedings of International Forum on Medical Imaging in Asia 2021

      Volume: 1179207 Pages: 29-29

    • DOI

      10.1117/12.2590780

    • Related Report
      2021 Research-status Report
  • [Presentation] 体幹部CT画像における脊柱起立筋の区分化学習による腸腰筋の認識2023

    • Author(s)
      川本真大,神谷直希,周向栄,原武史,藤田広志
    • Organizer
      第21回情報学ワークショップ(WiNF2023)
    • Related Report
      2023 Annual Research Report
  • [Presentation] CT画像における骨格筋の同時学習による骨位置推定を要しない胸鎖乳突筋の自動認識法2023

    • Author(s)
      芦野公祐,神谷直希,周向栄,原武史,藤田広志
    • Organizer
      第21回情報学ワークショップ(WiNF2023)
    • Related Report
      2023 Annual Research Report
  • [Presentation] 体幹部CT画像における2D U-Netを用いた大域構造5領域の認識2023

    • Author(s)
      芦野公祐,神谷直希,周向栄,加藤博基,原武史,藤田広志
    • Organizer
      医用画像情報学会(MII)令和5年度年次(第196回)大会
    • Related Report
      2023 Annual Research Report
  • [Presentation] Automatic Segmentation of Superficial Skeletal Muscles by 2D U-Net Using Simultaneous Learning of Bones by Virtual Unfolded CT Images2023

    • Author(s)
      S. Miyamoto, N. Kamiya, X. Zhou, H. Kato, T. Hara, and H. Fujita
    • Organizer
      International Forum on Medical Imaging in Asia
    • Related Report
      2022 Research-status Report
    • Int'l Joint Research
  • [Presentation] Skeletal Muscle Segmentation in L3 Cross Section by 2D U-Net Using Simultaneous Learning of Skeletal Muscles in Body CT Images2023

    • Author(s)
      M. Kawamoto, N. Kamiya, X. Zhou, H. Kato, T. Hara, and H. Fujita
    • Organizer
      International Forum on Medical Imaging in Asia
    • Related Report
      2022 Research-status Report
    • Int'l Joint Research
  • [Presentation] 自己教師あり学習に基づく全身CT画像からの骨格筋の自動抽出に関する研究2023

    • Author(s)
      野﨑孝太,周向栄,神谷直希,原武史,藤田広志
    • Organizer
      医用画像情報学会(MII)令和4年度春季(第195回)大会
    • Related Report
      2022 Research-status Report
  • [Presentation] Skeletal muscle segmentation by simultaneous learning of particular superficial back muscles using 2D UNet in torso CT images2022

    • Author(s)
      M. Kawamoto, N. Kamiya, X. Zhou, H. Kato, T. Hara and H. Fujita
    • Organizer
      36th International Congress and Exhibition on Computer Assisted Radiology
    • Related Report
      2022 Research-status Report
    • Int'l Joint Research
  • [Presentation] 体幹部CT 画像における2D U-Net を用いた体腔の同時学習による腰方形筋の自動認識2022

    • Author(s)
      川本真大,神谷直希,周 向栄,加藤博基,原 武史,三好利治,松尾政之,藤田広志
    • Organizer
      第41回日本医用画像工学会大会
    • Related Report
      2022 Research-status Report
  • [Presentation] 仮想展開画像を用いた2D U-Netにおける僧帽筋と隣接する骨格筋の認識2022

    • Author(s)
      宮本桜,川本真大,神谷直希,周向栄,加藤博基,原武史,藤田広志
    • Organizer
      医用画像情報学会(MII)令和4年度秋季(第194回)大会
    • Related Report
      2022 Research-status Report
  • [Presentation] 3次元DeepCNNによる全身CT画像からの骨格筋領域の自動抽出に関する基礎的な検討2021

    • Author(s)
      野﨑孝太,周向栄,神谷直希,原武史,藤田広志
    • Organizer
      電子情報通信学会技術研究報告
    • Related Report
      2021 Research-status Report
  • [Presentation] 体幹部CT画像におけるU-Netを用いた脊柱起立筋と僧帽筋の同時自動認識2021

    • Author(s)
      加藤彰,神谷直希,周向栄,加藤博基,原武史,藤田広志
    • Organizer
      医用画像情報学会(MII)令和3年度秋季(第191回)大会
    • Related Report
      2021 Research-status Report
  • [Book] Bone Joint Nerve2021

    • Author(s)
      神谷直希
    • Total Pages
      310
    • Publisher
      アークメディア
    • Related Report
      2021 Research-status Report
  • [Remarks] 神谷直希研究室のWebページ

    • URL

      https://www.ist.aichi-pu.ac.jp/~n-kamiya/

    • Related Report
      2023 Annual Research Report
  • [Remarks] Google Scholar

    • URL

      https://scholar.google.co.jp/citations?hl=ja&user=VzYGJekAAAAJ&view_op=list_works&sortby=pubdate

    • Related Report
      2022 Research-status Report 2021 Research-status Report
  • [Remarks] 愛知県立大学情報科学部神谷直希研究室

    • URL

      http://www.ist.aichi-pu.ac.jp/~n-kamiya/

    • Related Report
      2022 Research-status Report 2021 Research-status Report

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Published: 2021-04-28   Modified: 2025-01-30  

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