Key result
New CPAISD dataset provides segmented NCCT imaging to accelerate hyperacute ischemic stroke identification.
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The CPAISD dataset provides annotated NCCT images to facilitate the development of machine learning models for early detection of hyperacute ischemic stroke.
We introduce the CPAISD: Core-Penumbra hyperAcute Ischemic Stroke Dataset, aimed at enhancing the early detection of ischemic stroke using Non-Contrast Computed Tomography (NCCT) scans. Hyperacute ischemic stroke is the earliest phase of stroke, characterized by subtle or absent findings on NCCT scans, which complicates timely diagnosis. The dataset provides a collection of segmented NCCT images, including annotations of ischemic core and penumbra regions, critical for developing machine learning models for rapid stroke identification and assessment. By offering a carefully collected and annotated dataset, we aim to facilitate the development of advanced diagnostic tools, contributing to improved patient care and outcomes in stroke management. Our dataset uniquely focuses on the hyperacute phase, where NCCT scans typically show minimal or no signs of stroke, and includes a baseline model demonstrating its utility.
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Umerenkov et al. (2025) studied Hyperacute ischemic stroke. Core-Penumbra hyperAcute Ischemic Stroke Dataset (CPAISD) was evaluated. The Core-Penumbra hyperAcute Ischemic Stroke Dataset (CPAISD) provides segmented NCCT images with annotations of ischemic core and penumbra regions to facilitate rapid stroke identification.
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