  Surgical tool segmentation and action recognition are fundamental building
blocks in many computer-assisted intervention applications, ranging from
surgical skills assessment to decision support systems. Nowadays,
learning-based action recognition and segmentation approaches outperform
classical methods, relying, however, on large, annotated datasets. Furthermore,
action recognition and tool segmentation algorithms are often trained and make
predictions in isolation from each other, without exploiting potential
cross-task relationships. With the EndoVis 2022 SAR-RARP50 challenge, we
release the first multimodal, publicly available, in-vivo, dataset for surgical
action recognition and semantic instrumentation segmentation, containing 50
suturing video segments of Robotic Assisted Radical Prostatectomy (RARP). The
aim of the challenge is twofold. First, to enable researchers to leverage the
scale of the provided dataset and develop robust and highly accurate
single-task action recognition and tool segmentation approaches in the surgical
domain. Second, to further explore the potential of multitask-based learning
approaches and determine their comparative advantage against their single-task
counterparts. A total of 12 teams participated in the challenge, contributing 7
action recognition methods, 9 instrument segmentation techniques, and 4
multitask approaches that integrated both action recognition and instrument
segmentation. The complete SAR-RARP50 dataset is available at:
https://rdr.ucl.ac.uk/projects/SARRARP50_Segmentation_of_surgical_instrumentation_and_Action_Recognition_on_Robot-Assisted_Radical_Prostatectomy_Challenge/191091
