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Despite recent improvements in medical image segmentation, the ability togeneralize across imaging contrasts remains an open issue. To tackle thischallenge, we implement Feature-wise Linear Modulation (FiLM) to leveragephysics knowledge within the segmentation model and learn the characteristicsof each contrast. Interestingly, a well-optimised U-Net reached the sameperformance as our FiLMed-Unet on a multi-contrast dataset (0.72 of Dicescore), which suggests that there is a bottleneck in spinal MS lesionsegmentation different from the generalization across varying contrasts. Thisbottleneck likely stems from inter-rater variability, which is estimated at0.61 of Dice score in our dataset.

Automatic segmentation of spinal multiple sclerosis lesions: How to generalize across MRI contrasts?