PitVis-2023 challenge: Workflow recognition in videos of endoscopic pituitary surgery
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...
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| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
December 2025
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| In: |
Medical image analysis
Year: 2025, Volume: 106, Pages: 1-18 |
| ISSN: | 1361-8423 |
| DOI: | 10.1016/j.media.2025.103716 |
| Online Access: | Verlag, kostenfrei, Volltext: https://doi.org/10.1016/j.media.2025.103716 Verlag, kostenfrei, Volltext: https://www.sciencedirect.com/science/article/pii/S1361841525002634 
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| Author Notes: | 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, Stefanie Speidel, Alejandra Pérez, Santiago Rodriguez, Pablo Arbeláez, Danail Stoyanov, Hani J. Marcus, Sophia Bano |
| Summary: | 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 or during live surgery. The Pituitary Vision (PitVis) 2023 Challenge tasks the community to step and instrument recognition in videos of endoscopic pituitary surgery. This is a particularly challenging 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. The top performing model for step recognition utilised a transformer based architecture, uniquely using an autoregressive decoder with a positional encoding input. The top performing model for instrument recognition utilised a spatial encoder followed by a temporal encoder, which uniquely used a 2-layer temporal architecture. In both cases, these models outperformed purely spatial based models, illustrating the importance of sequential and temporal information. This PitVis-2023 therefore demonstrates state-of-the-art computer vision models in minimally invasive surgery are transferable to a new dataset. 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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| Item Description: | Gesehen am 11.03.2026 |
| Physical Description: | Online Resource |
| ISSN: | 1361-8423 |
| DOI: | 10.1016/j.media.2025.103716 |