The current deep learning based instance segmentation methods cannot fully train the network model and result in sub-optimal segmentation results due to the lack of labeled engine blade data. To improve the precision of aeroengine blade instance segmentation， an aeroengine blade instance segmentation method based on incomplete instance guidance was proposed. Combining with an existing instance segmentation method and an interactive segmentation method， promising aeroengine blade instance segmentation results were obtained. First， a small amount of labeled data was used to train the instance segmentation network， which generated initial instance segmentation results of aeroengine blades. Secondly， the detected single blade instance was divided into foreground and background. By selecting foreground seed points and background seed points， the interactive segmentation method was used to generate complete segmentation results of the blade. After all the blade instances were processed in turn， the final segmentation result of engine blade instance was obtained by merging the results. All the 72 images were used to train the Sparse Instance activation map for real-time instance segmentation （SparseInst）， to produce the initial instance segmentation results. The testing dataset contained 56 images. The mean Average Precision （mAP） of the proposed method is higher than that of SparseInst by 5.1 percentage points. The mAP results of the proposed method are better than those of the state-of-the-art instance segmentation methods， e.g.， MASK R-CNN （Mask Region based Convolutional Neural Network）， YOLACT （You Only Look At CoefficienTs）， BMASK-RCNN （Boundary-preserving MASK R-CNN）.