An automatic segmentation of liver lesion is one of the essential processes for computer-aided diagnosis for screening of liver diseases. In this study, we proposed an in-house, reinforced U-Net, i.e., the ‘CT attenuation-integrated U-Net (CAIUNet)’ as a new deep learning model for automatic segmentation of focal liver lesion in abdominal CT imaging. The CAIUNet is based on the basic U-Net. The CAI-UNet is focused on the CT attenuation value, which is significant information to differentiate between healthy tissues and lesions in CT imaging, but could not be directly included after passing through several convolutional operations in the basic U-Net. We introduced directly the connection between outputs of the last 3x3 convolution and a raw input image to enhance CT attenuation information. For training CAI-UNet, the weighted dice loss function was used to solve the imbalance of target lesions. For evaluation, we used LiTS challenge dataset of 131 abdominal CT which contained various focal liver lesions, and selected 90 sets containing liver metastasis by a radiologist with more than 30 years of experience. For statistical analysis, we performed a paired t-test to compare the lesion segmentation accuracy. Our results showed that CAI-UNet in performing liver lesion segmentation yielded 0.646 global dice score, 0.543 subject dice score, 0.568 specificity score and 0.651 precision score on test dataset. We also found that the CAI-UNet showed the significant improvement for segmentation of liver lesions compared with U-Net only (P < 0.05). The proposed CAI-UNet could be used to improve the detection of liver lesion.