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14 November 2023 Ischemic stroke outcome prediction based on the whole-brain features extracted from minimum intensity projection of DSC-PWI images by pre-trained Med3D network
Yingwei Guo, Yingjian Yang, Nanrong Zeng, Mingming Wang, Yu Luo, Jiaxi Lu, Xueqiang Zeng, Shicong Wang, Xiaoqiang Miao, Wenxin Duan, Anbo Cao, Huihui Yang, Yan Kang
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Proceedings Volume 12934, Third International Conference on Computer Graphics, Image, and Virtualization (ICCGIV 2023); 129340W (2023) https://doi.org/10.1117/12.3008207
Event: 2023 3rd International Conference on Computer Graphics, Image and Virtualization (ICCGIV 2023), 2023, Nanjing, China
Abstract
Accurate stroke outcome prediction is of great significance to making treatment plans and evaluating the rehabilitation state of patients. Previous works paid more attention to the basic information and volume of ischemic tissue for predicting outcomes, ignoring the role of the whole-brain. The purpose of this paper was to prove the value of wholebrain features in outcome prediction. In detail, the pre-trained Med3D model was used to extract whole-brain features from minimum intensity projection (MinIP) of PWI-DSC images, the Least absolute shrinkage and selection operator was used to select outstanding whole-brain features, and ten machine learning models were applied to validate the role of the selected outstanding whole-brain features on predicting outcomes. As the results, when taking ResNet10, ResNet18, ResNet34, and ResNet50 as encoders in the Med3D model, the best AUC of outstanding whole-brain features were 0.88, 0.939, 0.781, and 0.883, and the mean ± std on the ten machine models were 0.756 ± 0.097, 0.766 ± 0.123, 0.714 ± 0.044, and 0.761 ± 0.105, respectively. It can be concluded that the outstanding whole-brain features extracted from the MinIP image can predict good outcomes and poor outcomes for ischemic stroke patients, and the whole-brain features from ResNet18 performed best. The method provided in this study may provide new insight for ischemic stroke research.
(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Yingwei Guo, Yingjian Yang, Nanrong Zeng, Mingming Wang, Yu Luo, Jiaxi Lu, Xueqiang Zeng, Shicong Wang, Xiaoqiang Miao, Wenxin Duan, Anbo Cao, Huihui Yang, and Yan Kang "Ischemic stroke outcome prediction based on the whole-brain features extracted from minimum intensity projection of DSC-PWI images by pre-trained Med3D network", Proc. SPIE 12934, Third International Conference on Computer Graphics, Image, and Virtualization (ICCGIV 2023), 129340W (14 November 2023); https://doi.org/10.1117/12.3008207
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KEYWORDS
Feature extraction
Ischemic stroke
Image segmentation
Brain tissue
Image registration
3D image processing
Feature selection
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