- Tytuł:
- PitVis-2023 challenge: Workflow recognition in videos of endoscopic pituitary surgery
- Autorzy:
-
Ye, Jin
Płotka, Szymon
Speidel, Stefanie
Das, Adrito
He, Junjun
Qayyum, Abdul
Zou, Xiaoyang
Zhang, Yitong
Razzak, Imran
Stoyanov, Danail
Chen, Zhen
Mazher, Moona
Li, Tianbin
Pang, You
Zheng, Guoyan
Pérez, Alejandra
Kasai, Satoshi
Kaleta, Joanna
Marcus, Hani J.
Vasconcelos, Francisco
Rivoir, Dominik
Wu, Jinlin
Khan, Danyal Z.
Hirasawa, Kousuke
Bano, Sophia
Jund, Antoine
Yamlahi, Amine
Psychogyios, Dimitrios
Rodriguez, Santiago
Arbeláez, Pablo
Hanrahan, John G.
Kondo, Satoshi
Godau, Patrick - Opis:
- 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
- Dostawca treści:
- Repozytorium Uniwersytetu Jagiellońskiego
Artykuł