Machine learning-based segmentation of ischemic penumbra by using diffusion tensor metrics in a rat model
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12929_2020_Article_672.pdf
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Author(s)
Kuo, Duen-Pang
Kuo, Po-Chih
Chen, Yung-Chieh
Kao, Yu-Chieh J
Lee, Ching-Yen
Chung, Hsiao-Wen
Chen, Cheng-Yu
Date Issued
July 15, 2020
Publisher
BioMed Central
Citation
Journal of Biomedical Science. 2020 Jul 15;27(1):80
Version
Final published version
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.
Methods
Fourteen male rats subjected to permanent middle cerebral artery occlusion (pMCAO) were included in this study. Using a 7 T magnetic resonance imaging, DTI metrics such as fractional anisotropy, pure anisotropy, diffusion magnitude, mean diffusivity (MD), axial diffusivity, and radial diffusivity were derived. The MD and relative cerebral blood flow maps were coregistered to define the IP and IC at 0.5 h after pMCAO. A 2-level classifier was proposed based on DTI-derived metrics to classify stroke hemispheres into the IP, IC, and normal tissue (NT). The classification performance was evaluated using leave-one-out cross validation.
Results
The IC and non-IC can be accurately segmented by the proposed 2-level classifier with an area under the receiver operating characteristic curve (AUC) between 0.99 and 1.00, and with accuracies between 96.3 and 96.7%. For the training dataset, the non-IC can be further classified into the IP and NT with an AUC between 0.96 and 0.98, and with accuracies between 95.0 and 95.9%. For the testing dataset, the classification accuracy for IC and non-IC was 96.0 ± 2.3% whereas for IP and NT, it was 80.1 ± 8.0%. Overall, we achieved the accuracy of 88.1 ± 6.7% for classifying three tissue subtypes (IP, IC, and NT) in the stroke hemisphere and the estimated lesion volumes were not significantly different from those of the ground truth (p = .56, .94, and .78, respectively).
Conclusions
Our method achieved comparable results to the conventional approach using perfusion–diffusion mismatch. We suggest that a single DTI sequence along with ML algorithms is capable of dichotomizing ischemic tissue into the IC and IP.
MIT Department
Massachusetts Institute of Technology. Institute for Medical Engineering & Science
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DOI of Published Version
https://doi.org/10.1186/s12929-020-00672-9
