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

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

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...

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Bibliographic Details
Main Authors: Kuo, Duen-Pang, Kuo, Po-Chih, Chen, Yung-Chieh, Kao, Yu-Chieh J, Lee, Ching-Yen, Chung, Hsiao-Wen, Chen, Cheng-Yu
Other Authors: Massachusetts Institute of Technology. Institute for Medical Engineering & Science
Format: Article
Language:English
Published: BioMed Central 2021
Online Access:https://hdl.handle.net/1721.1/131575

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https://hdl.handle.net/1721.1/131575

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