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In this work, we present a comparison of a shallow and a deep learningarchitecture for the automated segmentation of white matter lesions in MRimages of multiple sclerosis patients. In particular, we train and test bothmethods on early stage disease patients, to verify their performance inchallenging conditions, more similar to a clinical setting than what istypically provided in multiple sclerosis segmentation challenges. Furthermore,we evaluate a prototype naive combination of the two methods, which refines thefinal segmentation. All methods were trained on 32 patients, and the evaluationwas performed on a pure test set of 73 cases. Results show low lesion-wisefalse positives (30%) for the deep learning architecture, whereas the shallowarchitecture yields the best Dice coefficient (63%) and volume difference(19%). Combining both shallow and deep architectures further improves thelesion-wise metrics (69% and 26% lesion-wise true and false positive rate,respectively).

Shallow vs deep learning architectures for white matter lesion segmentation in the early stages of multiple sclerosis