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.
@article{arxiv.2203.10039,
title = {Multi-input segmentation of damaged brain in acute ischemic stroke patients using slow fusion with skip connection},
author = {Luca Tomasetti and Mahdieh Khanmohammadi and Kjersti Engan and Liv Jorunn Høllesli and Kathinka Dæhli Kurz},
journal= {arXiv preprint arXiv:2203.10039},
year = {2023}
}