Machine learning-based segmentation of ischemic penumbra by using diffusion tensor metrics in a rat model.

Journal: Journal of biomedical science
PMID: 32664906

Abstract

BACKGROUND: Recent trials have shown promise in intra-arterial thrombectomy after the first 6-24 h of stroke onset. Quick and precise identification of the salvageable tissue is essential for successful stroke management. In this study, we examined the feasibility of machine learning (ML) approaches for differentiating the ischemic penumbra (IP) from the infarct core (IC) by using diffusion tensor imaging (DTI)-derived metrics.

Authors

  • Duen-Pang Kuo
    Department of Medical Imaging, Taipei Medical University Hospital, No.250, Wu-Hsing St, Taipei, 11031, Taiwan.
  • Po-Chih Kuo
    Department of Computer Science, National Chiao Tung University, 1001 University Road, Hsinchu, Taiwan.
  • Yung-Chieh Chen
    Department of Medical Imaging, Taipei Medical University Hospital, No.250, Wu-Hsing St, Taipei, 11031, Taiwan.
  • Yu-Chieh Jill Kao
    Department of Biomedical Imaging and Radiological Sciences, National Yang-Ming University, No.155, Sec.2, Linong St, Taipei, 11221, Taiwan.
  • Ching-Yen Lee
    TMU Center for Big Data and Artificial Intelligence in Medical Imaging, Taipei Medical University Hospital, Taipei, Taiwan.
  • Hsiao-Wen Chung
    Graduate Institute of Biomedical Electrics and Bioinformatics, National Taiwan University, Taipei, Taiwan.
  • Cheng-Yu Chen
    Department of Medical Imaging, Taipei Medical University Hospital, No.250, Wu-Hsing St, Taipei, 11031, Taiwan. sandy0932@gmail.com.

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