Figure 2:

We aim to transfer the significant variability of contrast and morphology to a healthy-defined anatomical atlas target with linear and non-linear transformations for generalizing anatomical context across scales (top panel). The complete pipeline (lower panel) can be divided into two steps: 1) body part regression preprocessing and 2) deep supervised registration. We initially crop the abdominal area of interest for both atlas target and subject scans with the guidance of body part regression network. We downsample both volumes and input into a deep registration network to predict the voxel displacement across tri-planar perspective. We finally warp the predicted transformations to each subject scan and compute average map for analysis.