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. Author manuscript; available in PMC: 2018 Sep 1.

Published in final edited form as: Neuroimage. 2017 Jul 11;158:378–396. doi: 10.1016/j.neuroimage.2017.07.008

3D (probabilistic) network architecture. The network takes two 3D patches from the moving and target image as the input, and outputs 3 3D initial momentum patches (one for each of the x,y and z dimensions respectively; for readability, only one decoder branch is shown in the figure). In case of the deterministic network, see Sec. 2.2.1, the dropout layers, illustrated by , are removed. Conv: 3D convolution layer. Conv^T: 3D transposed convolution layer. Parameters for the Conv and Conv^T layers: In: input channel. Out: output channel. Kernel: 3D filter kernel size in each dimension. Stride: stride for the 3D convolution. Pad: zero-padding added to the boundaries of the input patch. Note that in this illustration B denotes the batch size.

Inline graphic — 3D (probabilistic) network architecture. The network takes two 3D patches from the moving and target image as the input, and outputs 3 3D initial momentum patches (one for each of the x,y and z dimensions respectively; for readability, only one decoder branch is shown in the figure). In case of the deterministic network, see Sec. 2.2.1, the dropout layers, illustrated by , are removed. Conv: 3D convolution layer. Conv^T: 3D transposed convolution layer. Parameters for the Conv and Conv^T layers: In: input channel. Out: output channel. Kernel: 3D filter kernel size in each dimension. Stride: stride for the 3D convolution. Pad: zero-padding added to the boundaries of the input patch. Note that in this illustration B denotes the batch size.