  Surgical instrument segmentation -- in general a pixel classification task --
is fundamentally crucial for promoting cognitive intelligence in robot-assisted
surgery (RAS). However, previous methods are struggling with discriminating
instrument types and instances. To address the above issues, we explore a mask
classification paradigm that produces per-segment predictions. We propose
TraSeTR, a novel Track-to-Segment Transformer that wisely exploits tracking
cues to assist surgical instrument segmentation. TraSeTR jointly reasons about
the instrument type, location, and identity with instance-level predictions
i.e., a set of class-bbox-mask pairs, by decoding query embeddings.
Specifically, we introduce the prior query that encoded with previous temporal
knowledge, to transfer tracking signals to current instances via identity
matching. A contrastive query learning strategy is further applied to reshape
the query feature space, which greatly alleviates the tracking difficulty
caused by large temporal variations. The effectiveness of our method is
demonstrated with state-of-the-art instrument type segmentation results on
three public datasets, including two RAS benchmarks from EndoVis Challenges and
one cataract surgery dataset CaDIS.
