Deep Learning of Image Features from Unlabeled Data for Multiple Sclerosis Lesion Segmentation

Abstract

A new automatic method for multiple sclerosis (MS) lesion segmentation in multi-channel 3D MR images is presented. The main novelty of the method is that it learns the spatial image features needed for training a supervised classifier entirely from unlabeled data. This is in contrast to other current supervised methods, which typically require the user to preselect or design the features to be used. Our method can learn an extensive set of image features with minimal user effort and bias. In addition, by separating the feature learning from the classifier training that uses labeled (pre-segmented data), the feature learning can take advantage of the typically much more available unlabeled data. Our method uses deep learning for feature learning and a random forest for supervised classification, but potentially any supervised classifier can be used. Quantitative validation is carried out using 1450 T2-weighted and PD-weighted pairs of MRIs of MS patients, with 1400 pairs used for feature learning (100 of those for labeled training), and 50 for testing. The results demonstrate that the learned features are highly competitive with hand-crafted features in terms of segmentation accuracy, and that segmentation performance increases with the amount of unlabeled data used, even when the number of labeled images is fixed.