no code implementations • 1 Jan 2023 • James Batten, Matthew Sinclair, Ben Glocker, Michiel Schaap
Extracting complex structures from grid-based data is a common key step in automated medical image analysis.
no code implementations • 30 Dec 2022 • Karim Kadry, Abhishek Karmakar, Andreas Schuh, Kersten Peterson, Michiel Schaap, David Marlevi, Charles Taylor, Elazer Edelman, Farhad Nezami
We formulate the problem in terms of finding the optimal \emph{virtual catheter path} that samples the CCTA data to recapitulate the coronary artery morphology found in the intravascular image.
no code implementations • 18 Dec 2020 • Matthew Sinclair, Andreas Schuh, Karl Hahn, Kersten Petersen, Ying Bai, James Batten, Michiel Schaap, Ben Glocker
We propose Atlas-ISTN, a framework that jointly learns segmentation and registration on 2D and 3D image data, and constructs a population-derived atlas in the process.
1 code implementation • 22 Jul 2019 • Matthew C. H. Lee, Ozan Oktay, Andreas Schuh, Michiel Schaap, Ben Glocker
The goal is to learn a complex function that maps the appearance of input image pairs to parameters of a spatial transformation in order to align corresponding anatomical structures.
2 code implementations • 22 Aug 2018 • Jo Schlemper, Ozan Oktay, Michiel Schaap, Mattias Heinrich, Bernhard Kainz, Ben Glocker, Daniel Rueckert
AGs can be easily integrated into standard CNN models such as VGG or U-Net architectures with minimal computational overhead while increasing the model sensitivity and prediction accuracy.