The use of Computed Tomography (CT) imaging for patients with stroke symptoms is an essential step for triaging and diagnosis in many hospitals. However, the subtle expression of ischemia in acute CT images has made it hard for automated methods to extract potentially quantifiable information. In this work, we present and evaluate an automated deep learning tool for acute stroke lesion core segmentation from CT and CT perfusion images. For evaluation, the Ischemic Stroke Lesion Segmentation (ISLES) 2018 challenge dataset is used that includes 94 cases for training and 62 for testing. The presented method is an improved version of our workshop challenge approach that was ranked among the workshop challenge finalists. The introduced contributions include a more regularized network training procedure, symmetric modality augmentation and uncertainty filtering. Each of these steps is quantitatively evaluated by cross-validation on the training set. Moreover, our proposal is evaluated against other state-of-the-art methods with a blind testing set evaluation using the challenge website, which maintains an ongoing leaderboard for fair and direct method comparison. The tool reaches competitive performance ranking among the top performing methods of the ISLES 2018 testing leaderboard with an average Dice similarity coefficient of 49%. In the clinical setting, this method can provide an estimate of lesion core size and location without performing time costly magnetic resonance imaging. The presented tool is made publicly available for the research community.