A method of automatically registering landmarks in a 3-dimensional medical image, the method comprising: obtaining a 3D image (101, 701); determining a set of search points based on a statistical atlas attached to a bounding box corresponding to a part of the 3D image; extracting features of the determined set of search points; forming a set of candidates for a landmark based on the extracted features; outputting a final position of the landmark based on the set of candidates; characterized in that the method further comprises: filtering the candidates and outputting remaining candidates among the candidates based on the filtering; and in that the outputting of the final position of the landmark is based on one of the remaining candidates, wherein: the extracting of the features of the determined set of search points comprises: selecting a context around each of the search points, and applying a multilayered feature extraction algorithm to the context; wherein the multilayered feature extraction algorithm is obtained based on a learning procedure, wherein the learning procedure comprises: preprocessing and marking a set of 3D medical images; forming a learning base based on the set of marked 3D medical images, comprising inserting precedents, obtained by application of spatial distortions, into the learning base; initializing layers, responsible for extraction of features, in the multilayered feature extraction algorithm by performing learning without a teacher; and performing learning with a teacher on a model of the multilayered feature extraction algorithm based on the set of marked 3D medical images. obtaining a 3D image (101, 701); determining a set of search points based on a statistical atlas attached to a bounding box corresponding to a part of the 3D image; extracting features of the determined set of search points; forming a set of candidates for a landmark based on the extracted features; outputting a final position of the landmark based on the set of candidates; characterized in that the method further comprises: filtering the candidates and outputting remaining candidates among the candidates based on the filtering; and in that the outputting of the final position of the landmark is based on one of the remaining candidates, wherein: filtering the candidates and outputting remaining candidates among the candidates based on the filtering; and in that the outputting of the final position of the landmark is based on one of the remaining candidates, wherein: the extracting of the features of the determined set of search points comprises: selecting a context around each of the search points, and applying a multilayered feature extraction algorithm to the context; wherein the multilayered feature extraction algorithm is obtained based on a learning procedure, wherein the learning procedure comprises: preprocessing and marking a set of 3D medical images; forming a learning base based on the set of marked 3D medical images, comprising inserting precedents, obtained by application of spatial distortions, into the learning base; initializing layers, responsible for extraction of features, in the multilayered feature extraction algorithm by performing learning without a teacher; and performing learning with a teacher on a model of the multilayered feature extraction algorithm based on the set of marked 3D medical images. preprocessing and marking a set of 3D medical images; forming a learning base based on the set of marked 3D medical images, comprising inserting precedents, obtained by application of spatial distortions, into the learning base; initializing layers, responsible for extraction of features, in the multilayered feature extraction algorithm by performing learning without a teacher; and performing learning with a teacher on a model of the multilayered feature extraction algorithm based on the set of marked 3D medical images.