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

arXiv: https://arxiv.org/abs/2003.04377 · HTML: https://mlsanity.com/paper/2003.04377/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Mar 2020.

## Built on by (most cited first, 3 of 3)

- [Applications of Deep Learning Techniques for Automated Multiple Sclerosis Detection Using Magnetic Resonance Imaging: A Review](https://mlsanity.com/paper/2105.04881/) (2021)
- [Impact of individual rater style on deep learning uncertainty in medical imaging segmentation](https://mlsanity.com/paper/2105.02197/) (2021)
- [Benefits of Linear Conditioning with Metadata for Image Segmentation](https://mlsanity.com/paper/2102.09582/) (2021)

## Abstract (arXiv)

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.