Paper Info
Title
Physics-Informed Brain MRI Segmentation.
Abstract
Magnetic Resonance Imaging (MRI) is one of the most flexible and powerful medical imaging modalities. This flexibility does however come at a cost; MRI images acquired at different sites and with different parameters exhibit significant differences in contrast and tissue appearance, resulting in downstream issues when quantifying brain anatomy or the presence of pathology. In this work, we propose to combine multiparametric MRI-based static-equation sequence simulations with segmentation convolutional neural networks (CNN), to make these networks robust to variations in acquisition parameters. Results demonstrate that, when given both the image and their associated physics acquisition parameters, CNNs can produce segmentations that exhibit robustness to acquisition variations. We also show that the proposed physics-informed methods can be used to bridge multi-centre and longitudinal imaging studies where imaging acquisition varies across a site or in time.
Year
DOI
Venue
2019
10.1007/978-3-030-32778-1_11
SASHIMI@MICCAI
DocType
Citations 
PageRank 
Conference
0
0.34
References 
Authors
7
7
Name
Order
Citations
PageRank
Pedro Borges101.01
Sudre Carole H.213212.86
Thomas Varsavsky332.13
David Thomas463.37
Ivana Drobnjak5736.04
Sébastien Ourselin62499237.61
Cardoso M. Jorge76413.70