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.
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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 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 |
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