A Combined EM and Visual Tracking Probabilistic Model for Robust Mosaicking: Application to Fetoscopy

Marcel Tella-Amo, Pankaj Daga, Francois Chadebecq, Stephen Thompson, Dzhoshkun I. Shakir, George Dwyer, Ruwan Wimalasundera, Jan Deprest, Danail Stoyanov, Tom Vercauteren, Sebastien Ourselin; Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2016, pp. 84-92

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.