There is a low efficiency of automatically measuring and analyzing plant anatomic phenotypes currently, which makes it difficult to well deal with the issue of extracting and recognizing the complex anatomical phenotypes. In order to solve this problem, a mask region convolutional neural network (Mask R-CNN) based instance segmentation model for microscopic images of the citrus main leaf veins was proposed. In this model, the deep residual network (ResNet50) and the feature pyramid network (FPN) were used as the backbone feature extraction network. In addition, a new region of interest Align (RoI-Align) layer was added to the Mask branch to improve the segmentation accuracy. The results showed that the network can accurately identify and segment pith, xylem, phloem and cortical cells, respectively, in the citrus main leaf veins. The average precision (IoU was 0.50) of the model for segmentation of pith, xylem, phloem and cortical cells was 98.9%, 89.8%, 95.7% and 97.2%, respectively, and the overall average precision (IoU was 0.50) for segmentation of the four tissue regions was 95.4%. The mean average precision of Mask R-CNN with adding RoI-Align to the Mask branch was improved by 1.6 percentage points compared with that without. The results showed that Mask R-CNN model presented good performance of recognition and segmentation of various tissue regions of citrus main leaf veins, which can provide technical support for citrus microscopic phenotyping.