UCL Discovery
UCL home » Library Services » Electronic resources » UCL Discovery

Physics-informed brain MRI segmentation

Borges, P; Sudre, C; Varsavsky, T; Thomas, D; Drobnjak, I; Ourselin, S; Cardoso, MJ; (2019) Physics-informed brain MRI segmentation. In: Burgos, N and Gooya, A and Svoboda, D, (eds.) SASHIMI 2019: Simulation and Synthesis in Medical Imaging. (pp. pp. 100-109). Springer: Shenzhen, China. Green open access

[thumbnail of SASHIMI (6).pdf]
Preview
Text
SASHIMI (6).pdf - Published Version

Download (498kB) | Preview

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.

Type: Proceedings paper
Title: Physics-informed brain MRI segmentation
Event: 4th International Workshop, SASHIMI 2019, Held in Conjunction with MICCAI 2019
ISBN-13: 9783030327774
Open access status: An open access version is available from UCL Discovery
DOI: 10.1007/978-3-030-32778-1_11
Publisher version: https://doi.org/10.1007/978-3-030-32778-1_11
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: MRI, Harmonization, Deep learning
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology > Brain Repair and Rehabilitation
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Cardiovascular Science
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Cardiovascular Science > Population Science and Experimental Medicine
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Cardiovascular Science > Population Science and Experimental Medicine > MRC Unit for Lifelong Hlth and Ageing
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Med Phys and Biomedical Eng
URI: https://discovery.ucl.ac.uk/id/eprint/10090203
Downloads since deposit
57Downloads
Download activity - last month
Download activity - last 12 months
Downloads by country - last 12 months

Archive Staff Only

View Item View Item