Multi-input segmentation of damaged brain in acute ischemic stroke patients using slow fusion with skip connection
Peer reviewed, Journal article
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OriginalversjonTomasetti, L., Khanmohammadi, M., Engan, K., Høllesli, L.J., Kurz, K.D. (2022) Multi-input segmentation of damaged brain in acute ischemic stroke patients using slow fusion with skip connection. Proceedings of the Northern Lights Deep Learning Workshop 22, 3. 10.7557/18.6223
Time is a fundamental factor during stroke treatments. A fast, automatic approach that segmentsthe ischemic regions helps treatment decisions. In clinical use today, a set of color-coded parametric maps generated from computed tomography perfusion (CTP) images are investigated manually to decide a treatment plan. We propose an automatic method based on a neural network using a set of parametric maps to segment the two ischemic regions (core and penumbra) in patients affected by acute ischemic stroke. Our model is based on a convolution-deconvolution bottleneck structure with multi-input and slow fusion. A loss function based on the focal Tversky index addresses the data imbalance issue. The proposed architecture demonstrates effective performance and results comparable to the ground truth annotated by neuroradiologists. A Dice coefficient of 0.81 for penumbra and 0.52 for core over the large vessel occlusion test set is achieved. The full implementation is available at: https://git.io/JtFGb.