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In this work we propose a multi-scale recurrent encoder-decoder architectureto predict the breathing induced organ deformation in future frames. The modelwas trained end-to-end from input images to predict a sequence of motionlabels. Targets were created by quantizing the displacement fields obtainedfrom deformable image registration. We report results using MRI free-breathingacquisitions from 12 volunteers. Experiments were aimed at investigating theproposed multi-scale design and the effect of increasing the number ofpredicted frames on the overall accuracy of the model. The proposed model wasable to predict vessel positions in the next temporal image with a meanaccuracy of 2.07 (2.95) mm showing increased performance in comparison withstate-of-the-art approaches.

Spatiotemporal motion prediction in free-breathing liver scans via a recurrent multi-scale encoder decoder