  Surgical instrument segmentation in robot-assisted surgery (RAS) - especially
that using learning-based models - relies on the assumption that training and
testing videos are sampled from the same domain. However, it is impractical and
expensive to collect and annotate sufficient data from every new domain. To
greatly increase the label efficiency, we explore a new problem, i.e., adaptive
instrument segmentation, which is to effectively adapt one source model to new
robotic surgical videos from multiple target domains, only given the annotated
instruments in the first frame. We propose MDAL, a meta-learning based dynamic
online adaptive learning scheme with a two-stage framework to fast adapt the
model parameters on the first frame and partial subsequent frames while
predicting the results. MDAL learns the general knowledge of instruments and
the fast adaptation ability through the video-specific meta-learning paradigm.
The added gradient gate excludes the noisy supervision from pseudo masks for
dynamic online adaptation on target videos. We demonstrate empirically that
MDAL outperforms other state-of-the-art methods on two datasets (including a
real-world RAS dataset). The promising performance on ex-vivo scenes also
benefits the downstream tasks such as robot-assisted suturing and camera
control.
