Can surgical simulation be used to train detection and classification of neural networks?

ABSTRACT: Computer-assisted interventions (CAI) aim to increase the effectiveness, precision and repeatability of procedures to improve surgical outcomes. The presence and motion of surgical tools is a key information input for CAI surgical phase recognition algorithms. Vision-based tool detection and recognition approaches are an attractive solution and can be designed to take advantage of the powerful deep learning paradigm that is rapidly advancing image recognition and classification. The challenge for such algorithms is the availability and quality of labelled data used for training. In this Letter, surgical simulation is used to train tool detection and segmentation based on deep convolutional neural networks and generative adversarial networks. The authors experiment with two network architec

SUBMITTER: Zisimopoulos O 

PROVIDER: S-EPMC5683210 | biostudies-literature | 2017 Oct

REPOSITORIES: biostudies-literature