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Synthetic and Real Inputs for Tool Segmentation in Robotic Surgery

Colleoni, E; Edwards, P; Stoyanov, D; (2020) Synthetic and Real Inputs for Tool Segmentation in Robotic Surgery. In: Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. (pp. pp. 700-710). Springer Nature: Cham, Switzerland. Green open access

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Abstract

Semantic tool segmentation in surgical videos is important for surgical scene understanding and computer-assisted interventions as well as for the development of robotic automation. The problem is challenging because different illumination conditions, bleeding, smoke and occlusions can reduce algorithm robustness. At present labelled data for training deep learning models is still lacking for semantic surgical instrument segmentation and in this paper we show that it may be possible to use robot kinematic data coupled with laparoscopic images to alleviate the labelling problem. We propose a new deep learning based model for parallel processing of both laparoscopic and simulation images for robust segmentation of surgical tools. Due to the lack of laparoscopic frames annotated with both segmentation ground truth and kinematic information a new custom dataset was generated using the da Vinci Research Kit (dVRK) and is made available.

Type: Proceedings paper
Title: Synthetic and Real Inputs for Tool Segmentation in Robotic Surgery
ISBN-13: 978-3-030-59715-3
Open access status: An open access version is available from UCL Discovery
DOI: 10.1007/978-3-030-59716-0_67
Publisher version: https://doi.org/10.1007/978-3-030-59716-0_67
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions.
Keywords: Instrument detection and segmentationm Surgical vision, Computer assisted interventions
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/10113753
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