A lightweight asymmetric U-Net framework for acute ischemic stroke lesion segmentation in CT and CTP images

•A patch-based, residual, asymmetric, encoder-decoder 2D CNN architecture with 11 convolutional layers and 84,217 trainable parameters is introduced.•Utilized a training strategy combining the Focal Tversky and Binary cross-entropy loss functions to overcome the class imbalance issue in CT images.•A voting mechanism is applied to the result predicted by the weight matrices to ensure stable results.•Compared the method’s performance on the testing dataset with that of selected high-performing methods from the ISLES 2018 challenge and achieved the second rank.