SHIIBA TAKUROU 椎葉拓郎

… Alternative Names

Shiiba Takuro 椎葉拓郎

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Researcher Number	30759501
Other IDs	https://orcid.org/0000-0001-5515-091X
Affiliation (Current)	2025: 藤田医科大学, 医療科学部, 准教授
Affiliation (based on the past Project Information) *help	2021 – 2023: 藤田医科大学, 医療科学部, 准教授 2020: 帝京大学, 公私立大学の部局等, 講師 2018 – 2019: 帝京大学, 福岡キャンパス, 講師
Review Section/Research Field	Principal Investigator Basic Section 52040:Radiological sciences-related
Keywords	Principal Investigator SPECT / パーキンソン病 / radiomics / 予後予測 / MRI / Radiomics / 非典型的パーキンソン症候群 / パーキンソン症候群 / 画像特徴量 / 特徴量 … More / 機械学習 / ドパミントランスポーター / 非定型パーキンソン症候群 / 自動分類 / 人工知能 Less

Research Projects
(2 results)
Research Products
(18 results)

Comprehensive prognosis of Parkinson's disease using multimodal radiomic featuresPrincipal Investigator
- Principal Investigator
  
  Takuro Shiiba
- Project Period (FY)
  2021 – 2023
- Research Category
  
  Grant-in-Aid for Early-Career Scientists
- Review Section
  
  Basic Section 52040:Radiological sciences-related
- Research Institution
  Fujita Health University
Challenge to automatic classification between Parkinson's disease and atypical Parkinson's syndrome by artificial intelligencePrincipal Investigator
- Principal Investigator
  
  Shiiba Takuro
- Project Period (FY)
  2018 – 2021
- Research Category
  
  Grant-in-Aid for Early-Career Scientists
- Review Section
  
  Basic Section 52040:Radiological sciences-related
- Research Institution
  Fujita Health University
  Teikyo University

All 2024 2023 2022 2021 2020 2019 2018

All Journal Article Presentation

[Journal Article] Radiomics Score Derived from T1-w/T2-w Ratio Image Can Predict Motor Symptom Progression in Parkinson’s Disease2024
- Author(s)
  Takuya Shimozono, Takuro Shiiba, Kazuki Takano
- Journal Title
  
  European Radiology
  
  Volume: －
- Peer Reviewed
- Data Source
  KAKENHI-PROJECT-21K15791
[Journal Article] Dopamine transporter single-photon emission computed tomography-derived radiomics signature for detecting Parkinson’s disease2022
- Author(s)
  Shiiba Takuro、Takano Kazuki、Takaki Akihiro、Suwazono Shugo
- Journal Title
  
  EJNMMI Research
  
  Volume: 12 Issue: 1 Pages: 12-39
- DOI
  10.1186/s13550-022-00910-1
- Peer Reviewed / Open Access
- Data Source
  KAKENHI-PROJECT-21K15791
[Journal Article] 7. Applications of Machine Learning on Nuclear Medicine2021
- Author(s)
  Shiiba Takuro
- Journal Title
  
  Japanese Journal of Radiological Technology
  
  Volume: 77 Issue: 2 Pages: 193-199
- DOI
  10.6009/jjrt.2021_JSRT_77.2.193
- NAID
  130007988398
- ISSN
  0369-4305, 1881-4883
- Language
  Japanese
- Data Source
  KAKENHI-PROJECT-18K15565
[Journal Article] Improvement of classification performance of Parkinson’s disease using shape features for machine learning on dopamine transporter single photon emission computed tomography2020
- Author(s)
  Shiiba Takuro、Arimura Yuki、Nagano Miku、Takahashi Tenma、Takaki Akihiro
- Journal Title
  
  PLOS ONE
  
  Volume: 15 Issue: 1 Pages: e0228289-e0228289
- DOI
  10.1371/journal.pone.0228289
- Peer Reviewed / Open Access
- Data Source
  KAKENHI-PROJECT-18K15565
[Presentation] T1強調/T2強調比画像のradiomics特徴量を用いたパーキンソン病の運動症状の進行度予測の可能性2023
- Author(s)
  下薗卓也，椎葉拓郎，伊藤柊二，高野一輝
- Organizer
  第79回日本放射線技術学会総会学術大会
- Data Source
  KAKENHI-PROJECT-21K15791
[Presentation] Magnetic Resonance T1w/T2w Ratio-Based Radiomics Score for Predicting Clinical Motor Outcomes in Parkinson’s Disease.2023
- Author(s)
  Takuya Shimozono and Takuro Shiiba
- Organizer
  AAPM 65th Annual Meeting
- Int'l Joint Research
- Data Source
  KAKENHI-PROJECT-21K15791
[Presentation] Does radiomics score obtained from dopamine transporter SPECT reflect the severity of Parkinson's disease?2023
- Author(s)
  Takuro Shiiba
- Organizer
  SNMMI2023
- Int'l Joint Research
- Data Source
  KAKENHI-PROJECT-21K15791
[Presentation] パーキンソン病におけるdopamine transporter SPECT画像の特徴量と認知機能の関連性.2022
- Author(s)
  伊藤柊二，椎葉拓郎，下薗卓也, 高野一輝
- Organizer
  第42回日本核医学技術学会総会学術大会
- Data Source
  KAKENHI-PROJECT-21K15791
[Presentation] ドパミントランスポータSPECTにおける脳テンプレートを用いた3次元radiomics特徴量の有用性2021
- Author(s)
  伊藤柊二，椎葉拓郎，高野一輝
- Organizer
  第41回日本核医学技術学会総会学術大会
- Data Source
  KAKENHI-PROJECT-18K15565
[Presentation] ドパミントランスポータSPECT画像への人工知能応用の可能性2021
- Author(s)
  椎葉拓郎
- Organizer
  第49回日本放射線技術学会秋季学術大会
- Data Source
  KAKENHI-PROJECT-18K15565
[Presentation] ドパミントランスポータSPECT画像への人工知能応用の可能性2021
- Author(s)
  椎葉拓郎
- Organizer
  第49回日本放射線技術学会秋季学術大会
- Data Source
  KAKENHI-PROJECT-21K15791
[Presentation] Feasibility of Applying Super-Resolution Techniques Using Deep Learning to SPECT Images2021
- Author(s)
  T. Shiiba, M. Harada, I. Kaito
- Organizer
  European congress of radiology 2021
- Int'l Joint Research
- Data Source
  KAKENHI-PROJECT-18K15565
[Presentation] ドパミントランスポータSPECT画像特徴量を用いたパーキンソン病の運動機能予後予測性能改善の可能性2020
- Author(s)
  椎葉拓郎 , 高木昭浩
- Organizer
  第76回日本放射線技術学会
- Data Source
  KAKENHI-PROJECT-18K15565
[Presentation] Development of classification method using automatic shape extraction for dopamine transporter SPECT image2019
- Author(s)
  椎葉拓郎，中村優花，中村太一，高木昭浩
- Organizer
  第75回日本放射線技術学会総会学術大会
- Data Source
  KAKENHI-PROJECT-18K15565
[Presentation] Comparison of diagnostic performance of deep convolutional neural network using fine-tuning and feature extraction on dopamine transporter single photon emission tomography images2019
- Author(s)
  Takuro Shiiba, Akihiro Takaki
- Organizer
  SNMMI2019
- Int'l Joint Research
- Data Source
  KAKENHI-PROJECT-18K15565
[Presentation] Usefulness of Classification of Amyloid PET Images by Use of Textural Features2018
- Author(s)
  Shiiba T., Takahashi T., Nagano M., Takaki A.
- Organizer
  European Association of Nuclear Medicine
- Int'l Joint Research
- Data Source
  KAKENHI-PROJECT-18K15565
[Presentation] ドパミントランスポータSPECT画像の線条体自動抽出方の開発2018
- Author(s)
  中村太一、椎葉拓郎、中村優花、高木昭浩
- Organizer
  第13回九州放射線医療技術学術大会
- Data Source
  KAKENHI-PROJECT-18K15565
[Presentation] ドパミントランスポータSPECT画像の機械学習を用いたグレード分類の可能性2018
- Author(s)
  椎葉拓郎, 有村勇輝, 今村宏次郎, 高橋典馬, 永野未来, 貞苅将司, 高木昭浩
- Organizer
  第74回日本放射線技術学会総会学術大会
- Data Source
  KAKENHI-PROJECT-18K15565

SHIIBA TAKUROU 椎葉 拓郎

Shiiba Takuro 椎葉 拓郎

Research Projects

Research Products

Comprehensive prognosis of Parkinson's disease using multimodal radiomic featuresPrincipal Investigator

Principal Investigator

Project Period (FY)

Research Category

Review Section

Research Institution

Challenge to automatic classification between Parkinson's disease and atypical Parkinson's syndrome by artificial intelligencePrincipal Investigator

Principal Investigator

Project Period (FY)

Research Category

Review Section

Research Institution

[Journal Article] Radiomics Score Derived from T1-w/T2-w Ratio Image Can Predict Motor Symptom Progression in Parkinson’s Disease2024

Author(s)

Journal Title

Data Source

[Journal Article] Dopamine transporter single-photon emission computed tomography-derived radiomics signature for detecting Parkinson’s disease2022

Author(s)

Journal Title

DOI

Data Source

[Journal Article] 7. Applications of Machine Learning on Nuclear Medicine2021

Author(s)

Journal Title

DOI

NAID

ISSN

Language

Data Source

[Journal Article] Improvement of classification performance of Parkinson’s disease using shape features for machine learning on dopamine transporter single photon emission computed tomography2020

Author(s)

Journal Title

DOI

Data Source

[Presentation] T1強調/T2強調比画像のradiomics特徴量を用いたパーキンソン病の運動症状の進行度予測の可能性2023

Author(s)

Organizer

Data Source

[Presentation] Magnetic Resonance T1w/T2w Ratio-Based Radiomics Score for Predicting Clinical Motor Outcomes in Parkinson’s Disease.2023

Author(s)

Organizer

Data Source

[Presentation] Does radiomics score obtained from dopamine transporter SPECT reflect the severity of Parkinson's disease?2023

Author(s)

Organizer

Data Source

[Presentation] パーキンソン病におけるdopamine transporter SPECT画像の特徴量と認知機能の関連性.2022

Author(s)

Organizer

Data Source

[Presentation] ドパミントランスポータSPECTにおける脳テンプレートを用いた3次元radiomics特徴量の有用性2021

Author(s)

Organizer

Data Source

[Presentation] ドパミントランスポータSPECT画像への人工知能応用の可能性2021

Author(s)

Organizer

Data Source

[Presentation] ドパミントランスポータSPECT画像への人工知能応用の可能性2021

Author(s)

Organizer

Data Source

[Presentation] Feasibility of Applying Super-Resolution Techniques Using Deep Learning to SPECT Images2021

Author(s)

Organizer

Data Source

[Presentation] ドパミントランスポータSPECT画像特徴量を用いたパーキンソン病の運動機能予後予測性能改善の可能性2020

Author(s)

Organizer

Data Source

[Presentation] Development of classification method using automatic shape extraction for dopamine transporter SPECT image2019

Author(s)

Organizer

Data Source

[Presentation] Comparison of diagnostic performance of deep convolutional neural network using fine-tuning and feature extraction on dopamine transporter single photon emission tomography images2019

Author(s)

SHIIBA TAKUROU 椎葉拓郎

Shiiba Takuro 椎葉拓郎