Dataset Information

Probabilistic visual and electromagnetic data fusion for robust drift-free sequential mosaicking: application to fetoscopy.

ABSTRACT: The most effective treatment for twin-to-twin transfusion syndrome is laser photocoagulation of the shared vascular anastomoses in the placenta. Vascular connections are extremely challenging to locate due to their caliber and the reduced field-of-view of the fetoscope. Therefore, mosaicking techniques are beneficial to expand the scene, facilitate navigation, and allow vessel photocoagulation decision-making. Local vision-based mosaicking algorithms inherently drift over time due to the use of pairwise transformations. We propose the use of an electromagnetic tracker (EMT) sensor mounted at the tip of the fetoscope to obtain camera pose measurements, which we incorporate into a probabilistic framework with frame-to-frame visual information to achieve globally consistent sequential mosaics. We parametrize the problem in terms of plane and camera poses constrained by EMT measurements to enforce global consistency while leveraging pairwise image relationships in a sequential fashion through the use of local bundle adjustment. We show that our approach is drift-free and performs similarly to state-of-the-art global alignment techniques like bundle adjustment albeit with much less computational burden. Additionally, we propose a version of bundle adjustment that uses EMT information. We demonstrate the robustness to EMT noise and loss of visual information and evaluate mosaics for synthetic, phantom-based and ex vivo datasets.

SUBMITTER: Tella-Amo M

PROVIDER: S-EPMC5822039 | biostudies-literature | 2018 Apr

REPOSITORIES: biostudies-literature

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Probabilistic visual and electromagnetic data fusion for robust drift-free sequential mosaicking: application to fetoscopy.

Tella-Amo Marcel M Peter Loic L Shakir Dzhoshkun I DI Deprest Jan J Stoyanov Danail D Iglesias Juan Eugenio JE Vercauteren Tom T Ourselin Sebastien S

Journal of medical imaging (Bellingham, Wash.) 20180222 2

The most effective treatment for twin-to-twin transfusion syndrome is laser photocoagulation of the shared vascular anastomoses in the placenta. Vascular connections are extremely challenging to locate due to their caliber and the reduced field-of-view of the fetoscope. Therefore, mosaicking techniques are beneficial to expand the scene, facilitate navigation, and allow vessel photocoagulation decision-making. Local vision-based mosaicking algorithms inherently drift over time due to the use of ...[more]

PMID: 29487889

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Project description:BACKGROUND: Protein structure comparison is a central issue in structural bioinformatics. The standard dissimilarity measure for protein structures is the root mean square deviation (RMSD) of representative atom positions such as alpha-carbons. To evaluate the RMSD the structures under comparison must be superimposed optimally so as to minimize the RMSD. How to evaluate optimal fits becomes a matter of debate, if the structures contain regions which differ largely--a situation encountered in NMR ensembles and proteins undergoing large-scale conformational transitions. RESULTS: We present a probabilistic method for robust superposition and comparison of protein structures. Our method aims to identify the largest structurally invariant core. To do so, we model non-rigid displacements in protein structures with outlier-tolerant probability distributions. These distributions exhibit heavier tails than the Gaussian distribution underlying standard RMSD minimization and thus accommodate highly divergent structural regions. The drawback is that under a heavy-tailed model analytical expressions for the optimal superposition no longer exist. To circumvent this problem we work with a scale mixture representation, which implies a weighted RMSD. We develop two iterative procedures, an Expectation Maximization algorithm and a Gibbs sampler, to estimate the local weights, the optimal superposition, and the parameters of the heavy-tailed distribution. Applications demonstrate that heavy-tailed models capture differences between structures undergoing substantial conformational changes and can be used to assess the precision of NMR structures. By comparing Bayes factors we can automatically choose the most adequate model. Therefore our method is parameter-free. CONCLUSIONS: Heavy-tailed distributions are well-suited to describe large-scale conformational differences in protein structures. A scale mixture representation facilitates the fitting of these distributions and enables outlier-tolerant superposition.

Dataset Information

Probabilistic visual and electromagnetic data fusion for robust drift-free sequential mosaicking: application to fetoscopy.

Publications

Probabilistic visual and electromagnetic data fusion for robust drift-free sequential mosaicking: application to fetoscopy.

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