  Surgical scene segmentation is fundamentally crucial for prompting cognitive
assistance in robotic surgery. However, pixel-wise annotating surgical video in
a frame-by-frame manner is expensive and time consuming. To greatly reduce the
labeling burden, in this work, we study semi-supervised scene segmentation from
robotic surgical video, which is practically essential yet rarely explored
before. We consider a clinically suitable annotation situation under the
equidistant sampling. We then propose PGV-CL, a novel pseudo-label guided
cross-video contrast learning method to boost scene segmentation. It
effectively leverages unlabeled data for a trusty and global model
regularization that produces more discriminative feature representation.
Concretely, for trusty representation learning, we propose to incorporate
pseudo labels to instruct the pair selection, obtaining more reliable
representation pairs for pixel contrast. Moreover, we expand the representation
learning space from previous image-level to cross-video, which can capture the
global semantics to benefit the learning process. We extensively evaluate our
method on a public robotic surgery dataset EndoVis18 and a public cataract
dataset CaDIS. Experimental results demonstrate the effectiveness of our
method, consistently outperforming the state-of-the-art semi-supervised methods
under different labeling ratios, and even surpassing fully supervised training
on EndoVis18 with 10.1% labeling.
