Before revision:  The new architecture, which we call MultiPrior, was designed to be a fully-trainable, three-dimensional convolutional network. Thus, the resulting architecture represents a neural network with learnable spatial memories. When trained on a set of stroke patients and healthy subjects, MultiPrior outperforms the state-of-the-art segmentation tools such as DeepMedic and SPM segmentation.
After revision:  We train and test these networks on images of 43 stroke patients and healthy subjects, MultiPrior outperforms the state-of-the-art segmentation tools such as DeepMedic and SPM segmentation.