急性缺血性卒中组织命运特征的深度学习
Deep Learning of Tissue Fate Features in Acute Ischemic Stroke.
作者信息
Stier Noah, Vincent Nicholas, Liebeskind David, Scalzo Fabien
机构信息
Neurovascular Imaging Research Core, Department of Neurology, Univerisity of California, Los Angeles (UCLA).
出版信息
Proceedings (IEEE Int Conf Bioinformatics Biomed). 2015 Nov;2015:1316-1321. doi: 10.1109/BIBM.2015.7359869. Epub 2015 Dec 17.
Abstract
In acute ischemic stroke treatment, prediction of tissue survival outcome plays a fundamental role in the clinical decision-making process, as it can be used to assess the balance of risk vs. possible benefit when considering endovascular clot-retrieval intervention. For the first time, we construct a deep learning model of tissue fate based on randomly sampled local patches from the hypoperfusion (Tmax) feature observed in MRI immediately after symptom onset. We evaluate the model with respect to the ground truth established by an expert neurologist four days after intervention. Experiments on 19 acute stroke patients evaluated the accuracy of the model in predicting tissue fate. Results show the superiority of the proposed regional learning framework versus a single-voxel-based regression model.
摘要
在急性缺血性中风治疗中，组织存活结果的预测在临床决策过程中起着基础性作用，因为在考虑血管内血栓清除干预时，它可用于评估风险与潜在益处之间的平衡。我们首次基于症状发作后立即在MRI中观察到的低灌注（Tmax）特征的随机采样局部斑块构建了组织命运的深度学习模型。我们根据干预后四天由专家神经科医生确定的地面真值对模型进行评估。对19名急性中风患者的实验评估了该模型预测组织命运的准确性。结果表明，所提出的区域学习框架优于基于单像素的回归模型。
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