  Formalizing surgical activities as triplets of the used instruments, actions
performed, and target anatomies is becoming a gold standard approach for
surgical activity modeling. The benefit is that this formalization helps to
obtain a more detailed understanding of tool-tissue interaction which can be
used to develop better Artificial Intelligence assistance for image-guided
surgery. Earlier efforts and the CholecTriplet challenge introduced in 2021
have put together techniques aimed at recognizing these triplets from surgical
footage. Estimating also the spatial locations of the triplets would offer a
more precise intraoperative context-aware decision support for
computer-assisted intervention. This paper presents the CholecTriplet2022
challenge, which extends surgical action triplet modeling from recognition to
detection. It includes weakly-supervised bounding box localization of every
visible surgical instrument (or tool), as the key actors, and the modeling of
each tool-activity in the form of <instrument, verb, target> triplet. The paper
describes a baseline method and 10 new deep learning algorithms presented at
the challenge to solve the task. It also provides thorough methodological
comparisons of the methods, an in-depth analysis of the obtained results across
multiple metrics, visual and procedural challenges; their significance, and
useful insights for future research directions and applications in surgery.
