Abstract¶
Discerning surgical phases in laparoscopic surgeries is an essential
step for computer-assisted surgery (CAS) applications. Several past
studies utilize computer vision in the field of surgical workflow
analysis. Despite the promising results, the question of generalization
of such studies remains unclear and makes it hard for clinicians to
trust and adopt related applications in their practice.
In this work, we present the results of a study that includes 587
lap-cholecystectomy surgical videos which were manually annotated with
workflow surgical phases. This dataset, curated from five different
medical centers and performed by 49 different surgeons, is an order of
magnitude larger than all prior studies, it is captured from a much
larger variety of institutions and surgeons and was manually annotated
with surgical workflow phases.
This unique dataset makes it possible to train a state-of-the-art deep
spatio-temporal model which focuses on short and long term surgical
context. We then assess both the likely asymptotic performance of such a
workflow phase-detection system and evaluate how dataset size affects
the generalization of the system to new examples from unseen surgeons
and/or institutions.
Our results suggest that a model trained on several hundred samples can
generalize well and achieve above 90% frame-level accuracy while
maintaining unbiased results toward medical centers or surgeons.
Moreover, given a set of unseen videos from a new medical center, we
show that by fine-tuning on a relatively small number of new samples,
our model can converge to the same performance.
About Dotan Asselmann¶
Dotan
jumpstarted his professional career while serving as the computer vision
team leader of Israel's 81 military intelligence unit. After six years
of service, he co-founded EyeOnn, a startup tackling drowning events in
pools by leveraging computer vision. On a mission to make the world a
bit better, Dotan co-founded theator, and serves as the company's CTO.
He also heads Israel’s largest computer vision meet-up group (nearly
3000 members and growing).