A 10-layer 3D convolutional neural network architecture previously used to segment multiple sclerosis lesions. A cascaded training procedure was employed, training two separate networks to first identify candidate lesion voxels and next to reduce false positive detections. A third network re-trains the last fully connected layer to perform WMH segmentation.
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