  Performing low hertz labeling for surgical videos at intervals can greatly
releases the burden of surgeons. In this paper, we study the semi-supervised
instrument segmentation from robotic surgical videos with sparse annotations.
Unlike most previous methods using unlabeled frames individually, we propose a
dual motion based method to wisely learn motion flows for segmentation
enhancement by leveraging temporal dynamics. We firstly design a flow predictor
to derive the motion for jointly propagating the frame-label pairs given the
current labeled frame. Considering the fast instrument motion, we further
introduce a flow compensator to estimate intermediate motion within continuous
frames, with a novel cycle learning strategy. By exploiting generated data
pairs, our framework can recover and even enhance temporal consistency of
training sequences to benefit segmentation. We validate our framework with
binary, part, and type tasks on 2017 MICCAI EndoVis Robotic Instrument
Segmentation Challenge dataset. Results show that our method outperforms the
state-of-the-art semi-supervised methods by a large margin, and even exceeds
fully supervised training on two tasks.
