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.
Vega et al. (Tue,) studied this question.