  Time is a fundamental factor during stroke treatments. A fast, automatic
approach that segments the 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.
