PitVis-2023 Challenge: Workflow Recognition in videos of Endoscopic Pituitary Surgery

• URL: http://arxiv.org/abs/2409.01184v1
• Date: Mon, 2 Sep 2024 11:38:06 GMT
• Title: PitVis-2023 Challenge: Workflow Recognition in videos of Endoscopic Pituitary Surgery
• Authors: Adrito Das, Danyal Z. Khan, Dimitrios Psychogyios, Yitong Zhang, John G. Hanrahan, Francisco Vasconcelos, You Pang, Zhen Chen, Jinlin Wu, Xiaoyang Zou, Guoyan Zheng, Abdul Qayyum, Moona Mazher, Imran Razzak, Tianbin Li, Jin Ye, Junjun He, Szymon Płotka, Joanna Kaleta, Amine Yamlahi, Antoine Jund, Patrick Godau, Satoshi Kondo, Satoshi Kasai, Kousuke Hirasawa, Dominik Rivoir, Alejandra Pérez, Santiago Rodriguez, Pablo Arbeláez, Danail Stoyanov, Hani J. Marcus, Sophia Bano,
• Abstract summary: The Pituitary Vision (VisVis) 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. There were 18-s from 9-teams across 6-countries, using a variety of deep learning models.
• Score: 46.2901962659261
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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