Segmentation and extraction of birds contour is the premise of precision livestock farming management, such as behavior, health, welfare status monitoring based on machine vision technology. The precision and accuracy of image segmentation directly affect the reliability of relevant monitoring technology and decision-making. An instance segmentation approach based on Mask R-CNN deep learning framework was proposed to solve broiler instance segmentation and contour extraction problems in stacked-cage henhouse. Furthermore, a broiler image segmentation and contour extraction network was constructed to segment broiler images and realize birds individual contour extraction. In this network, totally 41 layers deep residual network (ResNet) based on attention mechanism and deformable convolution was integrated with feature pyramid networks (FPN) as the backbone network to extract the image features, and regions of interest were extracted by region proposal networks. Finally, target classification, segmentation and box regression were realized through network heads. Broiler image segmentation experiment showed that compared with Mask R-CNN network, the average precision and mean accuracy of the optimized network were improved from 78.23% and 84.48% to 88.60% and 90.37%, respectively, and the recall rate of the model was 77.48%, which can realize the pixel level segmentation of chicken contour. The research result can provide technical support for the real-time monitoring of birds welfare and health status.