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