Evaluating size of hyperacute stroke lesions speedily is an essential procedure before physicians make treatment decisions. For a patient with brain stroke suspicion, noncontrast computerized tomography (NCCT) is firstly taken for initial infarction assessment. However, in a lot of cases, because CT hypoattenuation and texture variation caused by hyperacute ischemia are subtle, besides local intensities and texture, physicians usually compare the difference between right and left sides based on the symmetric characteristic of brain anatomy not to miss the subtle lesions. In this paper, we propose a novel 3D U-Net architecture that integrates the comparison knowledge to automatically segment hyperacute stroke lesions on NCCT. To effectively capture right and left comparison features, we introduced a horizontal flip operation into 3D UNet. We also applied gradient-based sensitivity map method to our trained model in order to visualize how much each voxel contributes to segmentation results. Experimental results showed that the proposed architecture improved segmentation accuracy. Dice similarity coefficient (DSC) was improved from 0.44 to 0.54. Sensitivity and specificity was also improved from 0.80 to 1.00 and from 0.90 to 0.98 respectively. Sensitivity maps derived from our trained model demonstrated that both the right and left sides were utilized more effectively to successfully segment ischemic lesions.