  In this paper, an automatic algorithm aimed at volumetric segmentation of
acute ischemic stroke lesion in non-contrast computed tomography brain 3D
images is proposed. Our deep-learning approach is based on the popular 3D U-Net
convolutional neural network architecture, which was modified by adding the
squeeze-and-excitation blocks and residual connections. Robust pre-processing
methods were implemented to improve the segmentation accuracy. Moreover, a
specific patches sampling strategy was used to address the large size of
medical images, to smooth out the effect of the class imbalance problem and to
stabilize neural network training. All experiments were performed using
five-fold cross-validation on the dataset containing non-contrast computed
tomography volumetric brain scans of 81 patients diagnosed with acute ischemic
stroke. Two radiology experts manually segmented images independently and then
verified the labeling results for inconsistencies. The quantitative results of
the proposed algorithm and obtained segmentation were measured by the Dice
similarity coefficient, sensitivity, specificity and precision metrics. Our
proposed model achieves an average Dice of $0.628\pm0.033$, sensitivity of
$0.699\pm0.039$, specificity of $0.9965\pm0.0016$ and precision of
$0.619\pm0.036$, showing promising segmentation results.
