  Magnetic resonance imaging is a fundamental tool to reach a diagnosis of
multiple sclerosis and monitoring its progression. Although several attempts
have been made to segment multiple sclerosis lesions using artificial
intelligence, fully automated analysis is not yet available. State-of-the-art
methods rely on slight variations in segmentation architectures (e.g. U-Net,
etc.). However, recent research has demonstrated how exploiting temporal-aware
features and attention mechanisms can provide a significant boost to
traditional architectures. This paper proposes a framework that exploits an
augmented U-Net architecture with a convolutional long short-term memory layer
and attention mechanism which is able to segment and quantify multiple
sclerosis lesions detected in magnetic resonance images. Quantitative and
qualitative evaluation on challenging examples demonstrated how the method
outperforms previous state-of-the-art approaches, reporting an overall Dice
score of 89% and also demonstrating robustness and generalization ability on
never seen new test samples of a new dedicated under construction dataset.
