  The performance of deep learning (DL) algorithms is heavily influenced by the
quantity and the quality of the annotated data. However, in Surgical Data
Science, access to it is limited. It is thus unsurprising that substantial
research efforts are made to develop methods aiming at mitigating the scarcity
of annotated SDS data. In parallel, an increasing number of Computer Assisted
Interventions (CAI) datasets are being released, although the scale of these
remain limited. On these premises, data curation is becoming a key element of
many SDS research endeavors. Surgical video datasets are demanding to curate
and would benefit from dedicated support tools. In this work, we propose to
summarize surgical videos into storyboards or collages of representative frames
to ease visualization, annotation, and processing. Video summarization is
well-established for natural images. However, state-of-the-art methods
typically rely on models trained on human-made annotations, few methods have
been evaluated on surgical videos, and the availability of software packages
for the task is limited. We present videosum, an easy-to-use and open-source
Python library to generate storyboards from surgical videos that contains a
variety of unsupervised methods.
