Bano, Sophia;
Vasconcelos, Francisco;
David, Anna L;
Deprest, Jan;
Stoyanov, Danail;
(2022)
Placental vessel-guided hybrid framework for fetoscopic mosaicking.
Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization
10.1080/21681163.2022.2154278.
(In press).
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Abstract
Fetoscopic laser photocoagulation is used to treat twin-to-twin transfusion syndrome; however, this procedure is hindered because of difficulty in visualising the intraoperative surgical environment due to limited surgical field-of-view, unusual placenta position, limited manoeuvrability of the fetoscope and poor visibility due to fluid turbidity and occlusions. Fetoscopic video mosaicking can create an expanded field-of-view image of the fetoscopic intraoperative environment, which could support the surgeons in localising the vascular anastomoses during the fetoscopic procedure. However, classical handcrafted feature matching methods fail on in vivo fetoscopic videos. An existing state-of-the-art method on fetoscopic mosaicking relies on vessel presence and fails when vessels are not present in the view. We propose a vessel-guided hybrid fetoscopic mosaicking framework that mutually benefits from a placental vessel-based registration and a deep learning-based dense matching method to optimise the overall performance. A selection mechanism is implemented based on vessels’ appearance consistency and photometric error minimisation for choosing the best pairwise transformation. Using the extended fetoscopy placenta dataset, we experimentally show the robustness of the proposed framework, over the state-of-the-art methods, even in vessel-free, low-textured, or low illumination non-planar fetoscopic views.
| Type: | Article |
|---|---|
| Title: | Placental vessel-guided hybrid framework for fetoscopic mosaicking |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1080/21681163.2022.2154278 |
| Publisher version: | https://doi.org/10.1080/21681163.2022.2154278 |
| Language: | English |
| Additional information: | © 2022 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
| Keywords: | Fetoscopy; vessel segmentation; deep learning; video mosaicking; twin-to-twin tran |
| 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 |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10162157 |
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