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Network Inpainting via Optimal Transport

Project description

Bridging the technological gap towards natural network image reconstruction

The precise digital reconstruction of natural networks such as blood vessels or plant roots is crucial to ensure the quality of simulation-driven predictions. However, these structures can often be accessed only via noninvasive imaging techniques, leading to artifacts that compromise the reliability of the data and the derived simulations. Funded by the Marie Skłodowska-Curie Actions programme, the NIOT project aims to provide the mathematical and technological solutions to reconstruct networks from corrupted images. The core idea is to incorporate the most recent advances in branched transport theory within a variational image-processing method. The ultimate goal is to reconstruct the corrupted vascular networks in MRI scans of human patients.

Objective

"The precise digital reconstruction of natural networks such as blood vessels or plant roots is crucial to ensure the quality of
simulation-driven predictions. However, these structures can often be accessed only via noninvasive techniques, leading to artifacts
that compromise the reliability of the data and the derived simulations. No technological solution is currently able to recover digital
reconstructions of ""real"" networks from corrupted images.

The NIOT (Network Inpainting via Optimal Transport) project aims to fill this technological gap by defining for the first time a robust
mathematical formulation of the image network reconstruction problem. Thanks to the most recent advances of the optimal
transport theory, we will finally encode into equations the well-known fact that several natural networks are designed to transport
resources with the least effort possible. We will adopt a variational image processing method, where the reconstructed network is
obtained as the density minimizing the sum of the discrepancy with the observed data and a branch inducing functional. As such, our
proposed methodology builds a bridge between the image regularization and optimal transport communities.

A major ambition of the project is to pair the theoretical analysis with robust simulation tools that are capable of handling real data
arising from MRI acquisition techniques. This will require exploitation and development of dedicated components to handle large
datasets, both from a data handling and a multiscale simulation perspective. Our algorithm will be tested on a sequence of
increasingly channeling problems. We will start from simple synthetic networks, then we will use an high-quality map of the blood
vessel network of a mouse brain. The final benchmark will be to reconstruct of corrupted vascular networks in MRI scans of human
patients."

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Programme(s)

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Topic(s)

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Funding Scheme

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HORIZON-TMA-MSCA-PF-EF - HORIZON TMA MSCA Postdoctoral Fellowships - European Fellowships

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Call for proposal

Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.

(opens in new window) HORIZON-MSCA-2022-PF-01

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Coordinator

UNIVERSITETET I BERGEN
Net EU contribution

Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.

€ 210 911,04
Address
MUSEPLASSEN 1
5020 Bergen
Norway

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Region
Norge Vestlandet Vestland
Activity type
Higher or Secondary Education Establishments
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Total cost

The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.

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