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Deformable registration of two-dimensional/three-dimensional (2D/3D) imagesof abdominal organs is a complicated task because the abdominal organs deformsignificantly and their contours are not detected in two-dimensional X-rayimages. We propose a supervised deep learning framework that achieves 2D/3Ddeformable image registration between 3D volumes and single-viewpoint 2Dprojected images. The proposed method learns the translation from the target 2Dprojection images and the initial 3D volume to 3D displacement fields. Inexperiments, we registered 3D-computed tomography (CT) volumes to digitallyreconstructed radiographs generated from abdominal 4D-CT volumes. Forvalidation, we used 4D-CT volumes of 35 cases and confirmed that the 3D-CTvolumes reflecting the nonlinear and local respiratory organ displacement werereconstructed. The proposed method demonstrate the compatible performance tothe conventional methods with a dice similarity coefficient of 91.6 \% for theliver region and 85.9 \% for the stomach region, while estimating asignificantly more accurate CT values.

2D/3D Deep Image Registration by Learning 3D Displacement Fields for Abdominal Organs