Laparoscopy Computer Vision Project
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The original dataset is cited to: A.P. Twinanda, S. Shehata, D. Mutter, J. Marescaux, M. de Mathelin, N. Padoy, EndoNet: A Deep Architecture for Recognition Tasks on Laparoscopic Videos, IEEE Transactions on Medical Imaging (TMI), arXiv preprint, 2017
Here are a few use cases for this project:
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Surgical Training and Education: Utilizing the "Laparoscopy" computer vision model in surgical simulation tools and training materials to assist medical students and professionals in understanding the intricacies of a laparoscopic gallbladder surgery.
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Real-time Assistance in Laparoscopic Surgeries: Using the "Laparoscopy" model to provide real-time feedback and visual information guidance for surgeons during gallbladder removal surgery. This would help in minimizing the risk of complications and increasing the efficiency of the procedure.
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Enhanced Post-Surgery Analysis: Analyzing recordings of laparoscopic surgeries with the "Laparoscopy" model to retrospectively identify areas for improvement, document surgical outcomes, and gather data for future training or research purposes.
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Pre-Surgical Planning: Employing the "Laparoscopy" model on pre-operative imaging data to identify vital structures and assist surgeons in formulating a personalized surgical plan, reducing potential complications during the procedure.
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Development of Robotic-Assisted Surgical Systems: Integrating the "Laparoscopy" computer vision model in the development of autonomous or semi-autonomous robotic surgery systems, enabling them to accurately identify and interact with essential components during gallbladder-related laparoscopic procedures.
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Cite This Project
If you use this dataset in a research paper, please cite it using the following BibTeX:
@misc{
laparoscopy_dataset,
title = { Laparoscopy Dataset },
type = { Open Source Dataset },
author = { Laparoscopic YOLO },
howpublished = { \url{ https://universe.roboflow.com/laparoscopic-yolo/laparoscopy } },
url = { https://universe.roboflow.com/laparoscopic-yolo/laparoscopy },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2023 },
month = { jul },
note = { visited on 2024-11-21 },
}