In response to different noises in iris images, such as occlusion by glasses, blur, and angle deviation, this study designs a convolutional neural network (CNN) embedded with Mask R-CNN, named Mask-INet, for iris segmentation. The network adds a bottom-up path to the feature pyramid in the feature extraction stage, which not only improves the localization information of bottom-to-top features and enhances semantic information fusion but also further accelerates bottom-to-top propagation efficiency and effectively improves the accuracy of iris feature extraction. To further explore the feature information in the feature map, the study introduces upsampling and a convolutional block attention module (CBAM) network in the mask prediction branching stage. Upsampling is used to improve the spatial resolution of the feature map, and the CBAM network helps make the salient information in the feature map more significant so as to enhance the discrimination capacity for the features. The method is validated on the iris dataset provided by the NIR-ISL 2021 competition. The method outperforms the network of the champion of the event in terms of all indicators under the same experimental conditions. Compared with the baseline Mask R-CNN, the proposed method has the Dice similarity coefficient, mean intersection over union (mIoU), and recall improved by 8.53%, 11.97%, and 8.88%, respectively, which boosts iris segmentation performance.