  Accurate segmentation of tools in robot-assisted surgery is critical for
machine perception, as it facilitates numerous downstream tasks including
augmented reality feedback. While current feed-forward neural network-based
methods exhibit excellent segmentation performance under ideal conditions,
these models have proven susceptible to even minor corruptions, significantly
impairing the model's performance. This vulnerability is especially problematic
in surgical settings where predictions might be used to inform high-stakes
decisions. To better understand model behavior under non-adversarial
corruptions, prior work has explored introducing artificial corruptions, like
Gaussian noise or contrast perturbation to test set images, to assess model
robustness. However, these corruptions are either not photo-realistic or
model/task agnostic. Thus, these investigations provide limited insights into
model deterioration under realistic surgical corruptions. To address this
limitation, we introduce the SegSTRONG-C challenge that aims to promote the
development of algorithms robust to unforeseen but plausible image corruptions
of surgery, like smoke, bleeding, and low brightness. We collect and release
corruption-free mock endoscopic video sequences for the challenge participants
to train their algorithms and benchmark them on video sequences with
photo-realistic non-adversarial corruptions for a binary robot tool
segmentation task. This new benchmark will allow us to carefully study neural
network robustness to non-adversarial corruptions of surgery, thus constituting
an important first step towards more robust models for surgical computer
vision. In this paper, we describe the data collection and annotation protocol,
baseline evaluations of established segmentation models, and data
augmentation-based techniques to enhance model robustness.
