Advantages of brain parcellation in Multiple Sclerosis Lesion Segmentation

Segmentation of multiple sclerosis lesions plays an important role in understanding disease status. In this work, we focus on the effectiveness of brain parcellation in enhancing the performance of segmentation for multiple sclerosis lesions in Magnetic Resonance Imaging. Brain parcellation does not improve the segmentation performance, but make the results more robust in terms of overall variability (e.g. standard deviation), by dividing the brain into physically significant sub-regions that the model can concentrate on. Our approach combines parcellation with the existing diffusion-based model to increase sensitivity, particularly in regions with small anomalies. We conducted a thorough evaluation of a reference dataset on the field using all available modalities. Our results show how the parcellation of the brain when integrated into a diffusion-based pipeline, makes the segmentation of MS more stable, lowering deviations from the average, and improving some of the results w.r.t. state-of-the-art. This method achieves good segmentation capabilities even with small datasets, providing promising indications for further research.