To analyze the morphological parameters of Caenorhabditis elegans (C.elegans) automatedly and accurately by computers, the critical step is the segmentation of nematode body shape from the microscopic image. However, the design of C.elegans segmentation algorithm with robustness is still facing challenges because of a lot of noise in the microscopic image, the similarity between the pixels of the nematode edge with the surrounding environment, and the flagella and other attachments of the nematode body shape which need to be separated. Aiming at these problems, a method based on deep learning for nematode segmentation was proposed, in which the morphological features of nematodes were studied by training Mask Region-Convolutional Neural Network (Mask R-CNN) to realize automatic segmentation. Firstly, the high-level semantic features were combined with the low-level edge features by improving the multi-level feature pooling, and Large-Margin Softmax Loss (LMSL) algorithm was combined to improve the loss calculation. Then, the non-maximum suppression was improved. Finally, the methods such as fully connected fusion branch were added to further optimize the segmentation results. Experimental results show that compared to original Mask R-CNN, the proposed method has Average Precision (AP) increased by 4.3 percentage points, and the mean Intersection Over Union (mIOU) increased by 4 percentage points, which means that the proposed deep learning segmentation method can improve the segmentation accuracy effectively and segment the nematodes from the microscopic images more accurately.