ArXiv Preprint
This paper introduces the SurgT MICCAI 2022 challenge and its first results.
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, are provided. The participants
were tasked with the development of algorithms to track a bounding box on each
stereo endoscopic video. 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 and are
now available online. The teams were ranked according to their Expected Average
Overlap (EAO) score, which is a weighted average of Intersection over Union
(IoU) scores. The top team achieved an EAO score of 0.583 in the test subset.
Tracking soft-tissue using unsupervised algorithms was found to be achievable.
The dataset and benchmarking tool have been successfully created and made
publicly available online. This challenge is expected to contribute to the
development of autonomous robotic surgery, and other digital surgical
technologies.
Joao Cartucho, Alistair Weld, Samyakh Tukra, Haozheng Xu, Hiroki Matsuzaki, Taiyo Ishikawa, Minjun Kwon, Yongeun Jang, Kwang-Ju Kim, Gwang Lee, Bizhe Bai, Lueder Kahrs, Lars Boecking, Simeon Allmendinger, Leopold Muller, Yitong Zhang, Yueming Jin, Bano Sophia, Francisco Vasconcelos, Wolfgang Reiter, Jonas Hajek, Bruno Silva, Lukas R. Buschle, Estevao Lima, Joao L. Vilaca, Sandro Queiros, Stamatia Giannarou
2023-02-06
