  This paper introduces the ``SurgT: Surgical Tracking" challenge which was
organised in conjunction with MICCAI 2022. There were two purposes for the
creation of this challenge: (1) the establishment of the first standardised
benchmark for the research community to assess soft-tissue trackers; and (2) to
encourage the development of unsupervised deep learning methods, given the lack
of annotated data in surgery. A dataset of 157 stereo endoscopic videos from 20
clinical cases, along with stereo camera calibration parameters, have been
provided. Participants were assigned the task of developing algorithms to track
the movement of soft tissues, represented by bounding boxes, in stereo
endoscopic videos. At the end of the challenge, the developed methods were
assessed on a previously hidden test subset. This assessment uses benchmarking
metrics that were purposely developed for this challenge, to verify the
efficacy of unsupervised deep learning algorithms in tracking soft-tissue. The
metric used for ranking the methods was the Expected Average Overlap (EAO)
score, which measures the average overlap between a tracker's and the ground
truth bounding boxes. Coming first in the challenge was the deep learning
submission by ICVS-2Ai with a superior EAO score of 0.617. This method employs
ARFlow to estimate unsupervised dense optical flow from cropped images, using
photometric and regularization losses. Second, Jmees with an EAO of 0.583, uses
deep learning for surgical tool segmentation on top of a non-deep learning
baseline method: CSRT. CSRT by itself scores a similar EAO of 0.563. The
results from this challenge show that currently, non-deep learning methods are
still competitive. The dataset and benchmarking tool created for this challenge
have been made publicly available at https://surgt.grand-challenge.org/.
