1 code implementation • 13 Jun 2022 • Mikael Brudfors, Yael Balbastre, John Ashburner, Geraint Rees, Parashkev Nachev, Sebastien Ourselin, M. Jorge Cardoso
Data used in image segmentation are not always defined on the same grid.
1 code implementation • 19 Nov 2021 • Yu Yan, Yael Balbastre, Mikael Brudfors, John Ashburner
Segmentation of brain magnetic resonance images (MRI) into anatomical regions is a useful task in neuroimaging.
1 code implementation • 2 Feb 2021 • Yael Balbastre, Mikael Brudfors, Michela Azzarito, Christian Lambert, Martina F. Callaghan, John Ashburner
Instead, we propose a probabilistic generative (forward) model of the entire dataset, which is formulated and inverted to jointly recover (log) parameter maps with a well-defined probabilistic interpretation (e. g., maximum likelihood or maximum a posteriori).
1 code implementation • 24 Dec 2020 • Juan Eugenio Iglesias, Benjamin Billot, Yael Balbastre, Azadeh Tabari, John Conklin, Daniel C. Alexander, Polina Golland, Brian L. Edlow, Bruce Fischl
Most existing algorithms for automatic 3D morphometry of human brain MRI scans are designed for data with near-isotropic voxels at approximately 1 mm resolution, and frequently have contrast constraints as well - typically requiring T1 scans (e. g., MP-RAGE).
2 code implementations • 3 Sep 2019 • Mikael Brudfors, Yael Balbastre, Parashkev Nachev, John Ashburner
The model-driven nature of the approach means that no type of training is needed for applicability to the diversity of MR contrasts present in a clinical context.
no code implementations • 16 Aug 2019 • Mikael Brudfors, John Ashburner, Parashkev Nachev, Yael Balbastre
Automatically generating one medical imaging modality from another is known as medical image translation, and has numerous interesting applications.
4 code implementations • 8 Oct 2018 • Mikael Brudfors, Yael Balbastre, Parashkev Nachev, John Ashburner
This paper presents a generative model for super-resolution in routine clinical magnetic resonance images (MRI), of arbitrary orientation and contrast.
no code implementations • 27 Jul 2018 • John Ashburner, Mikael Brudfors, Kevin Bronik, Yael Balbastre
This paper presents a framework for automatically learning shape and appearance models for medical (and certain other) images.