We propose a novel method to automatically detect and segment multiple sclerosis lesions, located both in white matter and in the cortex. The algorithm consists of two main steps: (i) a supervised approach that outputs an initial bitmap locating candidates of lesional tissue and (ii) a Bayesian partial volume estimation framework that estimates the lesion concentration in each voxel. By using a “mixel” approach, potential partial volume effects especially affecting small lesions can be modeled, thus yielding improved lesion segmentation. The proposed method is tested on multiple MR image sequences including 3D MP2RAGE, 3D FLAIR, and 3D DIR. Quantitative evaluation is done by comparison with manual segmentations on a cohort of 39 multiple sclerosis early-stage patients.