Voxel-Wise Comparison with a-contrario Analysis for Automated Segmentation of Multiple Sclerosis Lesions from Multimodal MRI

Abstract

We introduce a new framework for the automated and unsupervised segmentation of Multiple Sclerosis lesions from multimodal Magnetic Resonance images. It relies on a voxel-wise approach to detect local white matter abnormalities, with an a-contrario analysis, which takes into account local information. First, a voxel-wise comparison of multimodal patient images to a set of controls is performed. Then, regionbased probabilities are estimated using an a-contrario approach. Finally, correction for multiple testing is performed. Validation was undertaken on a multi-site clinical dataset of 53 MS patients with various number and volume of lesions. We showed that the proposed framework outperforms the widely used FDR-correction for this type of analysis, particularly for low lesion loads.

Unsupervised Domain Adaptation With Optimal Transport in Multi-Site Segmentation of Multiple Sclerosis Lesions From MRI Data

“…Accurate identification of MS lesions in MRI images is extremely difficult due to variability in lesion location, size, and shape, in addition to anatomical variability across patients. Since manual segmentation requires expert knowledge, it is time consuming and prone to intra-and inter-expert variability, several methods have been proposed to automatically segment MS lesions (García-Lorenzo et al, 2013;Commowick et al, 2018;Galassi et al, 2018).…”