Multi-input segmentation of damaged brain in acute ischemic stroke patients using slow fusion with skip connection

نویسندگان

چکیده

Time is a fundamental factor during stroke treat-ments. A fast, automatic approach that segmentsthe ischemic regions helps treatment decisions. In clinical use today, set of color-coded parametric maps generated from computed tomography per-fusion (CTP) images are investigated manually to decide plan. We propose an method based on neural network using segment the two (core and penumbra) in patients affected by acute stroke. Our model convolution-deconvolution bottleneck structure with multi-input slow fusion. loss function focal Tversky index addresses data imbalance issue. The proposed architecture demon-strates effective performance results comparable ground truth annotated neuroradiologists. Dice coefficient 0.81 for penumbra 0.52 core over large vessel occlusion test achieved. full implementation available at: https://git.io/JtFGb.

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ژورنال

عنوان ژورنال: Proceedings of the Northern Lights Deep Learning Workshop

سال: 2022

ISSN: ['2703-6928']

DOI: https://doi.org/10.7557/18.6223