Optimized Automated Cardiac MR Scar Quantification with GAN-Based Data Augmentation

1 code implementation • 27 Sep 2021 • Didier R. P. R. M. Lustermans, Sina Amirrajab, Mitko Veta, Marcel Breeuwer, Cian M. Scannell

The mean DSC per-subject on the challenge test set, for the cascaded pipeline augmented by synthetic generated data, was 0. 86 (0. 03) and 0. 67 (0. 29) for myocardium and scar, respectively.

Data Augmentation

Physics-informed neural networks for myocardial perfusion MRI quantification

1 code implementation • 25 Nov 2020 • Rudolf L. M. van Herten, Amedeo Chiribiri, Marcel Breeuwer, Mitko Veta, Cian M. Scannell

This study introduces physics-informed neural networks (PINNs) as a means to perform myocardial perfusion MR quantification, which provides a versatile scheme for the inference of kinetic parameters.

Domain-Adversarial Learning for Multi-Centre, Multi-Vendor, and Multi-Disease Cardiac MR Image Segmentation

1 code implementation • 26 Aug 2020 • Cian M. Scannell, Amedeo Chiribiri, Mitko Veta

Deep learning has shown the potential to automate the requisite cardiac structure segmentation.

Image Segmentation Semantic Segmentation

Deep learning-based prediction of kinetic parameters from myocardial perfusion MRI

no code implementations • 27 Jul 2019 • Cian M. Scannell, Piet van den Bosch, Amedeo Chiribiri, Jack Lee, Marcel Breeuwer, Mitko Veta

The quantification of myocardial perfusion MRI has the potential to provide a fast, automated and user-independent assessment of myocardial ischaemia.

Bayesian Inference

Hierarchical Bayesian myocardial perfusion quantification

no code implementations • 6 Jun 2019 • Cian M. Scannell, Amedeo Chiribiri, Adriana D. M. Villa, Marcel Breeuwer, Jack Lee

Purpose: Tracer-kinetic models can be used for the quantitative assessment of contrast-enhanced MRI data.

Bayesian Inference