A computer-implemented method, comprising: receiving a first input medical image and a second input medical image depicting the same one or more anatomical objects in various states of deformation; for each respective anatomical object of the one or more anatomical objects: detecting a region of interest comprising the respective anatomical object in one of the first input medical image or the second input medical image, extracting the region of interest, respectively, from the first input medical image and from the second input medical image, wherein the extracted regions of interest are sub-images of the first input medical image and of the second input medical image, and determining a motion distribution of the respective anatomical object, the motion distribution being a probable motion for the respective anatomical object, by applying a motion model on the region of interest extracted from the first input medical image and the region of interest extracted from the second input medical image using the motion model, wherein the motion model is a learnt motion model specific to the respective anatomical object; and registering the first input medical image and the second input medical image based on the motion distribution of each respective anatomical object of the one or more anatomical objects, characterized in that registering the first input medical image and the second input medical image based on the motion distribution of each respective anatomical object of the one or more anatomical objects comprises: determining an anatomical object-specific regularization term for the one or more anatomical objects based on distances between the motion distribution of each respective anatomical object and a prior distribution of the respective anatomical object. receiving a first input medical image and a second input medical image depicting the same one or more anatomical objects in various states of deformation; for each respective anatomical object of the one or more anatomical objects: detecting a region of interest comprising the respective anatomical object in one of the first input medical image or the second input medical image, extracting the region of interest, respectively, from the first input medical image and from the second input medical image, wherein the extracted regions of interest are sub-images of the first input medical image and of the second input medical image, and determining a motion distribution of the respective anatomical object, the motion distribution being a probable motion for the respective anatomical object, by applying a motion model on the region of interest extracted from the first input medical image and the region of interest extracted from the second input medical image using the motion model, wherein the motion model is a learnt motion model specific to the respective anatomical object; and registering the first input medical image and the second input medical image based on the motion distribution of each respective anatomical object of the one or more anatomical objects, detecting a region of interest comprising the respective anatomical object in one of the first input medical image or the second input medical image, extracting the region of interest, respectively, from the first input medical image and from the second input medical image, wherein the extracted regions of interest are sub-images of the first input medical image and of the second input medical image, and determining a motion distribution of the respective anatomical object, the motion distribution being a probable motion for the respective anatomical object, by applying a motion model on the region of interest extracted from the first input medical image and the region of interest extracted from the second input medical image using the motion model, wherein the motion model is a learnt motion model specific to the respective anatomical object; and registering the first input medical image and the second input medical image based on the motion distribution of each respective anatomical object of the one or more anatomical objects, characterized in that registering the first input medical image and the second input medical image based on the motion distribution of each respective anatomical object of the one or more anatomical objects comprises: determining an anatomical object-specific regularization term for the one or more anatomical objects based on distances between the motion distribution of each respective anatomical object and a prior distribution of the respective anatomical object.