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AUTOMATIC SEGMENTATION OF ACUTE ISCHEMIC STROKE LESIONS IN COMPUTED TOMOGRAPHY DATA
AUTOMATIC SEGMENTATION OF ACUTE ISCHEMIC STROKE LESIONS IN COMPUTED TOMOGRAPHY DATA
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机译:X线断层扫描数据中急性缺血性卒中病变的自动分割
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摘要
Lesions associated with acute ischemic stroke are automatically segmented in images acquired with computed tomography (“CT”) using a trained machine learning algorithm (e.g., a neural network). The machine learning algorithm is trained on labeled data and associated CT data (e.g., non-contrast CT data and CT angiography source image (“CTA-SI”) data). The labeled data can include segmented data indicating lesions, which are generated by segmenting diffusion-weighted magnetic resonance images acquired within a specified time window from when the associated CT data were acquired. CT data (e.g., non-contrast CT data and CTA-SI data) acquired from a subject are then acquired and input to the trained machine learning algorithm to generate output as segmented CT data, which indicate lesions in the subject.
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