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To date, several automated strategies for identification/segmentation ofMultiple Sclerosis (MS) lesions with the use of Magnetic Resonance Imaging(MRI) have been presented, but they are outperformed by human experts, fromwhom they act very differently. This is mainly due to: the ambiguity originatedby MRI instabilities; peculiar variability of MS; non specificity of MRIregarding MS. Physicians partially manage the uncertainty generated byambiguity relying on radiological/clinical/anatomical background andexperience. To emulate human diagnosis, we present an automated framework foridentification/segmentation of MS lesions from MRI based on three pivotalconcepts: 1. the modelling of uncertainty; 2. the proposal of two, separatelytrained, CNN, one optimized for lesions and the other for lesions with respectto the environment surrounding them, respectively repeated for axial, coronaland sagittal directions; 3. the definition of an ensemble classifier to mergethe information collected by different CNN. The proposed framework is trained,validated and tested on the 2016 MSSEG benchmark public data set from a singleimaging modality, the FLuid-Attenuated Inversion Recovery (FLAIR). Thecomparison with the ground-truth and with each of 7 human raters, proves thatthere is no significant difference between the automated and the human raters.

Ensemble CNN and Uncertainty Modeling to Improve Automatic Identification/Segmentation of Multiple Sclerosis Lesions in Magnetic Resonance Imaging