  Despite recent improvements in medical image segmentation, the ability to
generalize across imaging contrasts remains an open issue. To tackle this
challenge, we implement Feature-wise Linear Modulation (FiLM) to leverage
physics knowledge within the segmentation model and learn the characteristics
of each contrast. Interestingly, a well-optimised U-Net reached the same
performance as our FiLMed-Unet on a multi-contrast dataset (0.72 of Dice
score), which suggests that there is a bottleneck in spinal MS lesion
segmentation different from the generalization across varying contrasts. This
bottleneck likely stems from inter-rater variability, which is estimated at
0.61 of Dice score in our dataset.
