  In this paper, we present a novel automated method for White Matter (WM)
lesion segmentation of Multiple Sclerosis (MS) patient images. Our approach is
based on a cascade of two 3D patch-wise convolutional neural networks (CNN).
The first network is trained to be more sensitive revealing possible candidate
lesion voxels while the second network is trained to reduce the number of
misclassified voxels coming from the first network. This cascaded CNN
architecture tends to learn well from small sets of training data, which can be
very interesting in practice, given the difficulty to obtain manual label
annotations and the large amount of available unlabeled Magnetic Resonance
Imaging (MRI) data. We evaluate the accuracy of the proposed method on the
public MS lesion segmentation challenge MICCAI2008 dataset, comparing it with
respect to other state-of-the-art MS lesion segmentation tools. Furthermore,
the proposed method is also evaluated on two private MS clinical datasets,
where the performance of our method is also compared with different recent
public available state-of-the-art MS lesion segmentation methods. At the time
of writing this paper, our method is the best ranked approach on the MICCAI2008
challenge, outperforming the rest of 60 participant methods when using all the
available input modalities (T1-w, T2-w and FLAIR), while still in the top-rank
(3rd position) when using only T1-w and FLAIR modalities. On clinical MS data,
our approach exhibits a significant increase in the accuracy segmenting of WM
lesions when compared with the rest of evaluated methods, highly correlating
($r \ge 0.97$) also with the expected lesion volume.
