Meeting Banner
Abstract #1620

Generating large-scale highly heterogenous synthetic MRIs for robust spinal cord segmentation models

Barbara Brito Vega1,2,3, Philipp Goebl1,2, Juan Eugenio Iglesias2,4,5, Sridar Narayanan6,7, Robin Wolz8, Frederik Barkhof1,2,9, and Arman Eshaghi1,2
1Department of Neuroinflammation, University College London, London, United Kingdom, 2Department of Computer Science, University College London, London, United Kingdom, 3Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom, 4Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States, 5Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Boston, MA, United States, 6McConnell Brain Imaging Centre, McGill University, Montreal, QC, Canada, 7Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada, 8IXICO plc, London, United Kingdom, 9Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centre, Amsterdam, Netherlands

Synopsis

Keywords: Spinal Cord, Segmentation, Generative models

Motivation: Manually labelling large data sets is infeasible, especially heterogenous clinical scans to train segmentation MRI models. Generative models can mitigate this problem.

Goal(s): Develop a generative model that introduces contrast and resolution variability into training datasets to segment heterogeneous clinical MRIs while reducing the need for manual labelling.

Approach: We developed a generative model to synthesise spinal MRI images from label maps, applying intensity modelling, spatial deformations and noise to simulate anatomical and contrast variations for training.

Results: We generated synthetic spinal MRIs and corresponding labels of varying contrasts and resolutions, showing higher similarity when mimicking original scans and lower when enabling contrast-randomisation.

Impact: Our model paves the way for training contrast-agnostic and resolution-independent MRI segmentation models for spinal cord. This facilitates the processing of routine care data supporting more robust, translatable and generalisable models which can impact patients with neurological disorders.

How to access this content:

For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.

After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.

After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.

Click here for more information on becoming a member.

Keywords