Vasconcelos, F;
Brandão, P;
Vercauteren, T;
Ourselin, S;
Deprest, J;
Peebles, D;
Stoyanov, D;
(2018)
Towards computer-assisted TTTS: Laser ablation detection for workflow segmentation from fetoscopic video.
International Journal of Computer Assisted Radiology and Surgery
10.1007/s11548-018-1813-8.
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Abstract
PURPOSE: Intrauterine foetal surgery is the treatment option for several congenital malformations. For twin-to-twin transfusion syndrome (TTTS), interventions involve the use of laser fibre to ablate vessels in a shared placenta. The procedure presents a number of challenges for the surgeon, and computer-assisted technologies can potentially be a significant support. Vision-based sensing is the primary source of information from the intrauterine environment, and hence, vision approaches present an appealing approach for extracting higher level information from the surgical site. METHODS: In this paper, we propose a framework to detect one of the key steps during TTTS interventions-ablation. We adopt a deep learning approach, specifically the ResNet101 architecture, for classification of different surgical actions performed during laser ablation therapy. RESULTS: We perform a two-fold cross-validation using almost 50 k frames from five different TTTS ablation procedures. Our results show that deep learning methods are a promising approach for ablation detection. CONCLUSION: To our knowledge, this is the first attempt at automating photocoagulation detection using video and our technique can be an important component of a larger assistive framework for enhanced foetal therapies. The current implementation does not include semantic segmentation or localisation of the ablation site, and this would be a natural extension in future work.
| Type: | Article |
|---|---|
| Title: | Towards computer-assisted TTTS: Laser ablation detection for workflow segmentation from fetoscopic video |
| Location: | Germany |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1007/s11548-018-1813-8 |
| Publisher version: | http://doi.org/10.1007/s11548-018-1813-8 |
| Language: | English |
| Additional information: | Copyright © The Author(s) 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. |
| Keywords: | Deep learning, Endoscopy, Twin-to-twin transfusion syndrome (TTTS), Workflow segmentation |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > UCL EGA Institute for Womens Health UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > UCL EGA Institute for Womens Health > Maternal and Fetal Medicine 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/10051883 |
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