UCL Discovery
UCL home » Library Services » Electronic resources » UCL Discovery

Real-Time Segmentation of Non-Rigid Surgical Tools based on Deep Learning and Tracking

Garcia Peraza Herrera, LC; Li, W; Gruijthuijsen, C; Devreker, A; Attilakos, G; Deprest, J; Vander Poorten, E; ... Ourselin, S; + view all (2017) Real-Time Segmentation of Non-Rigid Surgical Tools based on Deep Learning and Tracking. In: Computer-Assisted and Robotic Endoscopy. CARE 2016. Lecture Notes in Computer Science. (pp. pp. 84-95). Springer Verlag (Germany): Germany. Green open access

[img]
Preview
Text
paper.pdf - Accepted version

Download (17MB) | Preview

Abstract

Real-time tool segmentation is an essential component in computer-assisted surgical systems. We propose a novel real-time automatic method based on Fully Convolutional Networks (FCN) and optical flow tracking. Our method exploits the ability of deep neural networks to produce accurate segmentations of highly deformable parts along with the high speed of optical flow. Furthermore, the pre-trained FCN can be fine-tuned on a small amount of medical images without the need to hand-craft features. We validated our method using existing and new benchmark datasets, covering both ex vivo and in vivo real clinical cases where different surgical instruments are employed. Two versions of the method are presented, non-real-time and real-time. The former, using only deep learning, achieves a balanced accuracy of 89.6% on a real clinical dataset, outperforming the (non-real-time) state of the art by 3.8% points. The latter, a combination of deep learning with optical flow tracking, yields an average balanced accuracy of 78.2% across all the validated datasets.

Type: Proceedings paper
Title: Real-Time Segmentation of Non-Rigid Surgical Tools based on Deep Learning and Tracking
Event: CARE 2016, Computer-Assisted and Robotic Endoscopy: Third International Workshop, CARE 2016, Held in Conjunction with MICCAI 2016, 17-21 October 2016, Athens, Greece
Location: Athens
Dates: 17 October 2016 - 21 October 2016
ISBN-13: 978-3-319-54057-3
Open access status: An open access version is available from UCL Discovery
DOI: 10.1007/978-3-319-54057-3_8
Publisher version: http://dx.doi.org/10.1007/978-3-319-54057-3_8
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.
UCL classification: 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
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Med Phys and Biomedical Eng
URI: https://discovery.ucl.ac.uk/id/eprint/1508427
Downloads since deposit
428Downloads
Download activity - last month
Download activity - last 12 months
Downloads by country - last 12 months

Archive Staff Only

View Item View Item