PitVis-2023 Challenge: Endoscopic Pituitary Surgery videos
The first public dataset containing both step and instrument annotations of the endoscopic TransSphenoidal Approach (eTSA). The dataset includes 25-videos (video_{video_number}.mp4) and the corresponding step and instrument annotation (annotations_{video_number}.csv). Annotation metadata mapping the numerical value to its formal description is provided (map_steps.csv and map_instrument.csv), as well as video medadata (video_encoder_details.txt). Helpful scripts and baseline models can be found on: https://github.com/dreets/pitvis. This dataset is released as part of the PitVis Challenge, a sub-challenge of the EndoVis Challenge hosted at the annual MICCAI conference (Vancouver, Canada on 06-Oct-2024). More details about the challenge can be found on the challenge website: https://www.synapse.org/Synapse:syn51232283/wiki/621581. The companion paper with comparative models is titled: "PitVis-2023 Challenge: Workflow Recognition in videos of Endoscopic Pituitary Surgery" (Adrito Das et al.). Please cite this paper if you have used this dataset: https://arxiv.org/abs/2409.01184.
Funding
Context Aware Augmented Reality for Endonasal Endoscopic Surgery
Engineering and Physical Sciences Research Council
Find out more...AID-PitSurg: AI-enabled Decision support in Pituitary Surgery to reduce complications
Engineering and Physical Sciences Research Council
Find out more...Department of Science, Innovation and Technology (DSIT)
Royal Academy of Engineering under the Chair in Emerging Technologies programme
UKRI Centre for Doctoral Training in AI-enabled healthcare systems [EP/S021612/1]
NIHR Academic Clinical Fellowship [2021-18-009]
CRUK Pre-doctoral Fellowship [RCCPDB-Nov21/100007]
NIHR University College London Hospitals Biomedical Research Centre
National Institute for Health Research
Find out more...