Year: 2019
Type: preprint
Abstract: In mainstream computer vision and machine learning, public datasets such as ImageNet, COCO and KITTI have helped drive enormous improvements by enabling researchers to understand the strengths and limitations of different algorithms via performance comparison. However, this type of approach has had limited translation to problems in robotic assisted surgery as this field has never established the same level of common datasets and benchmarking methods. In 2015 a sub-challenge was introduced at the EndoVis workshop where a set of robotic images were provided with automatically generated annotations from robot forward kinematics. However, there were issues with this dataset due to the limited background variation, lack of complex motion and inaccuracies in the annotation. In this work we present the results of the 2017 challenge on robotic instrument segmentation which involved 10 teams participating in binary, parts and type based segmentation of articulated da Vinci robotic instruments.
Source: arXiv (Cornell University)
Authors: Allan, Max, Shvets, Alex, Kurmann, Thomas, Zhang, Zichen, Duggal, Rahul, Su, Yun-Hsuan, Rieke, Nicola, Laina, Iro, Kalavakonda, Niveditha, Bodenstedt, Sebastian, Herrera, Luis, Li, Wenqi, Iglovikov, Vladimir, Luo, Huoling, Yang, Jian, Stoyanov, Danail, Maier-Hein, Lena, Speidel, Stefanie, Azizian, Mahdi
Institutions: Intuitive Surgical (United States), Massachusetts Institute of Technology, University of Bern, University of Alberta, Georgia Institute of Technology, University of Washington, Technical University of Munich, National Center for Tumor Diseases, King's College London, Lyft (United States), Shenzhen Institutes of Advanced Technology, Beijing Institute of Technology, Wellcome / EPSRC Centre for Interventional and Surgical Sciences, University College London, German Cancer Research Center
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2913444875