Alabi, Oluwatosin;
Bano, Sophia;
Vasconcelos, Francisco;
David, Anna L;
Deprest, Jan;
Stoyanov, Danail;
(2022)
Robust fetoscopic mosaicking from deep learned flow fields.
International Journal of Computer Assisted Radiology and Surgery
, 17
pp. 1125-1134.
10.1007/s11548-022-02623-1.
Preview |
Text
Alabi2022_Article_RobustFetoscopicMosaickingFrom.pdf - Published Version Download (4MB) | Preview |
Abstract
PURPOSE: Fetoscopic laser photocoagulation is a minimally invasive procedure to treat twin-to-twin transfusion syndrome during pregnancy by stopping irregular blood flow in the placenta. Building an image mosaic of the placenta and its network of vessels could assist surgeons to navigate in the challenging fetoscopic environment during the procedure. METHODOLOGY: We propose a fetoscopic mosaicking approach by combining deep learning-based optical flow with robust estimation for filtering inconsistent motions that occurs due to floating particles and specularities. While the current state of the art for fetoscopic mosaicking relies on clearly visible vessels for registration, our approach overcomes this limitation by considering the motion of all consistent pixels within consecutive frames. We also overcome the challenges in applying off-the-shelf optical flow to fetoscopic mosaicking through the use of robust estimation and local refinement. RESULTS: We compare our proposed method against the state-of-the-art vessel-based and optical flow-based image registration methods, and robust estimation alternatives. We also compare our proposed pipeline using different optical flow and robust estimation alternatives. CONCLUSIONS: Through analysis of our results, we show that our method outperforms both the vessel-based state of the art and LK, noticeably when vessels are either poorly visible or too thin to be reliably identified. Our approach is thus able to build consistent placental vessel mosaics in challenging cases where currently available alternatives fail.
| Type: | Article |
|---|---|
| Title: | Robust fetoscopic mosaicking from deep learned flow fields |
| Location: | Germany |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1007/s11548-022-02623-1 |
| Publisher version: | https://doi.org/10.1007/s11548-022-02623-1 |
| Language: | English |
| Additional information: | This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. |
| Keywords: | Fetoscopy, Optical flow, Twin-to-twin transfusion syndrome, Video mosaicking |
| UCL classification: | UCL 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 > 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 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 |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10148144 |
Loading...
Loading...Loading...Loading...Archive Staff Only
 |
View Item |
