The field of computer vision applied to videos of minimally invasive surgery is ever-growing. Workflow recognition pertains to the automated recognition of various aspects of a surgery: including which surgical steps are performed; and which surgical instruments are used. This information can later be used to assist clinicians when learning the surgery; during live surgery; and when writing operation notes. The Pituitary Vision (PitVis) 2023 Challenge tasks the community to step and instrument recognition in videos of endoscopic pituitary surgery. This is a unique task when compared to other minimally invasive surgeries due to the smaller working space, which limits and distorts vision; and higher frequency of instrument and step switching, which requires more precise model predictions. Participants were provided with 25-videos, with results presented at the MICCAI-2023 conference as part of the Endoscopic Vision 2023 Challenge in Vancouver, Canada, on 08-Oct-2023. There were 18-submissions from 9-teams across 6-countries, using a variety of deep learning models. A commonality between the top performing models was incorporating spatio-temporal and multi-task methods, with greater than 50% and 10% macro-F1-score improvement over purely spacial single-task models in step and instrument recognition respectively. The PitVis-2023 Challenge therefore demonstrates state-of-the-art computer vision models in minimally invasive surgery are transferable to a new dataset, with surgery specific techniques used to enhance performance, progressing the field further. Benchmark results are provided in the paper, and the dataset is publicly available at: https://doi.org/10.5522/04/26531686.
@article{arxiv.2409.01184,
title = {PitVis-2023 Challenge: Workflow Recognition in videos of Endoscopic Pituitary Surgery},
author = {Adrito Das and Danyal Z. Khan and Dimitrios Psychogyios and Yitong Zhang and John G. Hanrahan and Francisco Vasconcelos and You Pang and Zhen Chen and Jinlin Wu and Xiaoyang Zou and Guoyan Zheng and Abdul Qayyum and Moona Mazher and Imran Razzak and Tianbin Li and Jin Ye and Junjun He and Szymon Płotka and Joanna Kaleta and Amine Yamlahi and Antoine Jund and Patrick Godau and Satoshi Kondo and Satoshi Kasai and Kousuke Hirasawa and Dominik Rivoir and Alejandra Pérez and Santiago Rodriguez and Pablo Arbeláez and Danail Stoyanov and Hani J. Marcus and Sophia Bano},
journal= {arXiv preprint arXiv:2409.01184},
year = {2024}
}