  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 conditions such as Longitudinal MS Lesion Segmentation
Challenge (ISBI Challenge). 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 group of compact 3D CNNs. This spatially distributed 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. DLB generalization was validated in
cross-dataset experiments on MSSEG'16, ISBI challenge, and in-house datasets.
During experiments, DLB showed higher segmentation accuracy, better
segmentation consistency and greater generalization performance compared to
state-of-the-art methods. Therefore, DLB offers a robust framework well-suited
for clinical practice.
