  Recently, segmentation methods based on Convolutional Neural Networks (CNNs)
showed promising performance in automatic Multiple Sclerosis (MS) lesions
segmentation. These techniques have even outperformed human experts in
controlled evaluation condition. However state-of-the-art approaches trained to
perform well on highly-controlled datasets fail to generalize on clinical data
from unseen datasets. Instead of proposing another improvement of the
segmentation accuracy, we propose a novel method robust to domain shift and
performing well on unseen datasets, called DeepLesionBrain (DLB). This
generalization property results from three main contributions. First, DLB is
based on a large ensemble of compact 3D CNNs. This ensemble strategy ensures a
robust prediction despite the risk of generalization failure of some individual
networks. Second, DLB includes a new image quality data augmentation to reduce
dependency to training data specificity (e.g., acquisition protocol). Finally,
to learn a more generalizable representation of MS lesions, we propose a
hierarchical specialization learning (HSL). HSL is performed by pre-training a
generic network over the whole brain, before using its weights as
initialization to locally specialized networks. By this end, DLB learns both
generic features extracted at global image level and specific features
extracted at local image level. At the time of publishing this paper, DLB is
among the Top 3 performing published methods on ISBI Challenge while using only
half of the available modalities. DLB generalization has also been compared to
other state-of-the-art approaches, during cross-dataset experiments on
MSSEG'16, ISBI challenge, and in-house datasets. DLB improves the segmentation
performance and generalization over classical techniques, and thus proposes a
robust approach better suited for clinical practice.
