Ant Colony Optimization with BrainSeg3D Protocol for Multiple Sclerosis Lesion Detection

Abstract

AbstractMagnetic resonance imaging (MRI) has quickly established itself as the reference imaging tool for the management of patients suffering from multiple sclerosis (MS), both for the diagnosis and the follow-up of the evolution and evaluation of the impact of new therapies.The treatment of multiple sclerosis does not cure the disease, but it slows its progression and can help to space out attacks. In this paper, tumor segmentation is treated as a problem of classification using the Ant Colony optimization algorithm (ACO) combined with a proposed protocol based on BrainSeg3D tools. Many studies and many existing approaches tend the multiple sclerosis (MS) which is a chronic inflammatory anomaly of the central nervous system.The aim of this work is to evaluate and to verify the effectiveness of the proposed protocol on a public longitudinal database which contains 20 MS patients. This study is concerned with comparing these results against the ground truth performed by two experts and against other methods namely Dissimilarity Map (DM) creation and segmentation in terms of Dice Similarity Coefficient (DSC).