A computer- implemented method of training a neural network for reconstructing of a 3D point cloud from at least one 2D image, comprising: extracting a plurality of point clouds each represented by an ordered list of coordinates, from a plurality of 3D anatomical images depicting a target anatomical structure; selecting one of the plurality of point clouds as a template; non-rigidly registering the template with each of the plurality of point clouds to compute a respective warped template having a shape of the respective point cloud and retaining the coordinate order of the template, wherein the plurality of warped templates are consistent in terms of coordinate order; receiving a plurality of 2D anatomical images depicting the target anatomical structure depicted in corresponding 3D anatomical images; and training a neural network, according to a training dataset of the warped templates and corresponding 2D images, for mapping at least one 2D anatomical image into a 3D point cloud; wherein each point cloud of the plurality of point clouds is extracted from a respective 3D anatomical image of the plurality of 3D anatomical images by: computing, for each 2D slice of a plurality of 2D slices of the respective 3D anatomical image, a 2D segmentation of the depicted target anatomical structure, stacking the plurality of 2D segmentations computed for the plurality of 2D slices, obtaining a 3D binary array of the target anatomical structure from the stacked plurality of 2D segmentations, and computing the point cloud from the 3D binary array. extracting a plurality of point clouds each represented by an ordered list of coordinates, from a plurality of 3D anatomical images depicting a target anatomical structure; selecting one of the plurality of point clouds as a template; non-rigidly registering the template with each of the plurality of point clouds to compute a respective warped template having a shape of the respective point cloud and retaining the coordinate order of the template, wherein the plurality of warped templates are consistent in terms of coordinate order; receiving a plurality of 2D anatomical images depicting the target anatomical structure depicted in corresponding 3D anatomical images; and training a neural network, according to a training dataset of the warped templates and corresponding 2D images, for mapping at least one 2D anatomical image into a 3D point cloud; wherein each point cloud of the plurality of point clouds is extracted from a respective 3D anatomical image of the plurality of 3D anatomical images by: computing, for each 2D slice of a plurality of 2D slices of the respective 3D anatomical image, a 2D segmentation of the depicted target anatomical structure, stacking the plurality of 2D segmentations computed for the plurality of 2D slices, obtaining a 3D binary array of the target anatomical structure from the stacked plurality of 2D segmentations, and computing the point cloud from the 3D binary array. computing, for each 2D slice of a plurality of 2D slices of the respective 3D anatomical image, a 2D segmentation of the depicted target anatomical structure, stacking the plurality of 2D segmentations computed for the plurality of 2D slices, obtaining a 3D binary array of the target anatomical structure from the stacked plurality of 2D segmentations, and computing the point cloud from the 3D binary array. <EOS>