Rapid and accurate evaluation of stroke is imperative as currently available treatments are constrained by a narrow time window. Diffusion Weighted Magnetic Resonance (DWI) is a key imaging modality in stroke evaluation as it allows for assessment of the extent of acute ischemic brain injury. Nonetheless, manual delineation of stroke regions is expensive, time-consuming, and subject to inter-rater variability. In this study, we sought to develop a deep learning approach for ischemic stroke volumetric segmentation in a large clinical dataset of 1,205 patients from the NIH-funded Heart-Brain Interactions in Human Acute Ischemic Stroke Study utilizing only DWI imaging.
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