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Articulated Multi-Instrument 2-D Pose Estimation Using Fully Convolutional Networks
2018
IEEE Transactions on Medical Imaging
Instrument detection, pose estimation and tracking in surgical videos is an important vision component for computer assisted interventions. While significant advances have been made in recent years, articulation detection is still a major challenge. In this paper, we propose a deep neural network for articulated multi-instrument 2D pose estimation, which is trained on a detailed annotations of endoscopic and microscopic datasets. Our model is formed by a fully convolutional detection-regression
doi:10.1109/tmi.2017.2787672
pmid:29727290
pmcid:PMC6051486
fatcat:pubjor5rlbgapou2m4bwt37bqi