  Automatic multiple sclerosis (MS) lesion segmentation using multi-contrast
magnetic resonance (MR) images provides improved efficiency and reproducibility
compared to manual delineation. Current state-of-the-art automatic MS lesion
segmentation methods utilize modified U-Net-like architectures. However, in the
literature, dedicated architecture modifications were always required to
maximize their performance. In addition, the best-performing methods have not
proven to be generalizable to diverse test datasets with contrast variations
and image artifacts. In this work, we developed an accurate and generalizable
MS lesion segmentation model using the well-known U-Net architecture without
further modification. A novel test-time self-ensembled lesion fusion strategy
is proposed that not only achieved the best performance using the ISBI 2015 MS
segmentation challenge data but also demonstrated robustness across various
self-ensemble parameter choices. Moreover, equipped with instance normalization
rather than batch normalization widely used in literature, the model trained on
the ISBI challenge data generalized well on clinical test datasets from
different scanners.
