  Context-aware decision support in the operating room can foster surgical
safety and efficiency by leveraging real-time feedback from surgical workflow
analysis. Most existing works recognize surgical activities at a coarse-grained
level, such as phases, steps or events, leaving out fine-grained interaction
details about the surgical activity; yet those are needed for more helpful AI
assistance in the operating room. Recognizing surgical actions as triplets of
<instrument, verb, target> combination delivers comprehensive details about the
activities taking place in surgical videos. This paper presents
CholecTriplet2021: an endoscopic vision challenge organized at MICCAI 2021 for
the recognition of surgical action triplets in laparoscopic videos. The
challenge granted private access to the large-scale CholecT50 dataset, which is
annotated with action triplet information. In this paper, we present the
challenge setup and assessment of the state-of-the-art deep learning methods
proposed by the participants during the challenge. A total of 4 baseline
methods from the challenge organizers and 19 new deep learning algorithms by
competing teams are presented to recognize surgical action triplets directly
from surgical videos, achieving mean average precision (mAP) ranging from 4.2%
to 38.1%. This study also analyzes the significance of the results obtained by
the presented approaches, performs a thorough methodological comparison between
them, in-depth result analysis, and proposes a novel ensemble method for
enhanced recognition. Our analysis shows that surgical workflow analysis is not
yet solved, and also highlights interesting directions for future research on
fine-grained surgical activity recognition which is of utmost importance for
the development of AI in surgery.
