An intensity-based thresholding method with region growing approach to segment periventricular and deep WMH separately, and two random forest classifiers for false positive reduction. Per imaging modality, 19 texture and 100 “multi-layer” features were computed. The “multi-layer” features were computed using a feed-forward convolutional network with fixed filters (e.g. averaging, Gaussian, Laplacian); consisting of two convolutional, two max-pooling, and one fully connected layer.

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