  The evaluation of white matter lesion progression is an important biomarker
in the follow-up of MS patients and plays a crucial role when deciding the
course of treatment. Current automated lesion segmentation algorithms are
susceptible to variability in image characteristics related to MRI scanner or
protocol differences. We propose a model that improves the consistency of MS
lesion segmentations in inter-scanner studies. First, we train a CNN base model
to approximate the performance of icobrain, an FDA-approved clinically
available lesion segmentation software. A discriminator model is then trained
to predict if two lesion segmentations are based on scans acquired using the
same scanner type or not, achieving a 78% accuracy in this task. Finally, the
base model and the discriminator are trained adversarially on multi-scanner
longitudinal data to improve the inter-scanner consistency of the base model.
The performance of the models is evaluated on an unseen dataset containing
manual delineations. The inter-scanner variability is evaluated on test-retest
data, where the adversarial network produces improved results over the base
model and the FDA-approved solution.
