A combined EM and visual tracking probabilistic model for robust mosaicking: application to fetoscopy
Conference paper
Tella-Amo, M., Daga, P., Chadebecq, F., Thompson, S., Shakir, D., Dwyer, G., Wimalasundera, R., Deprest, J., Stoyanov, D. and Vercauteren, T. 2016. A combined EM and visual tracking probabilistic model for robust mosaicking: application to fetoscopy. 2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). Las Vegas, NV, USA 26 Jun - 01 Jul 2016 IEEE. https://doi.org/10.1109/CVPRW.2016.72
Type | Conference paper |
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Title | A combined EM and visual tracking probabilistic model for robust mosaicking: application to fetoscopy |
Authors | Tella-Amo, M., Daga, P., Chadebecq, F., Thompson, S., Shakir, D., Dwyer, G., Wimalasundera, R., Deprest, J., Stoyanov, D. and Vercauteren, T. |
Abstract | Twin-to-Twin Transfusion Syndrome (TTTS) is a progressive pregnancy complication in which inter-twin vascular connections in the shared placenta result in a blood flow imbalance between the twins. The most effective therapy is to sever these connections by laser photo-coagulation. However, the limited field of view of the fetoscope hinders their identification. A potential solution is to augment the surgeon's view by creating a mosaic image of the placenta. State-of-the-art mosaicking methods use feature-based approaches, which have three main limitations: (i) they are not robust against corrupt data e.g. blurred frames, (ii) temporal information is not used, (iii) the resulting mosaic suffers from drift. We introduce a probabilistic temporal model that incorporates electromagnetic and visual tracking data to achieve a robust mosaic with reduced drift. By assuming planarity of the imaged object, the nRT decomposition can be used to parametrize the state vector. Finally, we tackle the non-linear nature of the problem in a numerically stable manner by using the Square Root Unscented Kalman Filter. We show an improvement in performance in terms of robustness as well as a reduction of the drift in comparison to state-of-the-art methods in synthetic, phantom and ex vivo datasets. |
Conference | 2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) |
Proceedings Title | 2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) |
Publisher | IEEE |
Publication dates | |
Online | 19 Dec 2016 |
Publication process dates | |
Accepted | 2016 |
Deposited | 28 Feb 2024 |
Output status | Published |
Digital Object Identifier (DOI) | https://doi.org/10.1109/CVPRW.2016.72 |
Web of Science identifier | WOS:000391572100065 |
Language | English |
https://repository.mdx.ac.uk/item/z657x
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