A computer-implemented method for automated spatially-consistent detection of anatomical landmarks in a medical image of a patient, comprising: generating a discrete scale-space representation of a medical image of a patient, wherein the discrete scale-space representation of the medical image includes a plurality of scale-levels; detecting a plurality of anatomical landmarks at a coarsest scale-level of the discrete scale-space representation of the medical image using a respective trained search model trained to predict a trajectory from a starting location to a predicted landmark location at the coarsest scale-level for each of the plurality of anatomical landmarks; enforcing spatial coherence of the detected anatomical landmarks by fitting a learned shape model of the plurality of anatomical landmarks to the detected anatomical landmarks at the coarsest scale-level to robustly determine a set of the anatomical landmarks within a field-of-view of the medical image; and refining the detected landmark location for each of the landmarks in the set of anatomical landmarks at each remaining scale-level of the discrete scale-space representation of the medical image using, for each landmark in the set of anatomical landmarks, a respective trained search model trained to predict a trajectory to a predicted landmark location at each remaining scale-level, wherein the trained search model for each remaining scale-level for each landmark is constrained based on a range surrounding the predicted landmark location for that landmark at a previous scale-level, characterized in that for each anatomical landmark, the trained search model for each scale-level is an intelligent artificial agent that predicts the trajectory to the predicted landmark location at the corresponding scale-level by iteratively using a trained deep neural network that inputs a region of interest surrounding a current location in the corresponding scale-level of the discrete scale-space representation of the medical image to calculate action-values corresponding to actions that move the current location in different direction and selecting an action having the highest action-value to be applied to the current location. generating a discrete scale-space representation of a medical image of a patient, wherein the discrete scale-space representation of the medical image includes a plurality of scale-levels; detecting a plurality of anatomical landmarks at a coarsest scale-level of the discrete scale-space representation of the medical image using a respective trained search model trained to predict a trajectory from a starting location to a predicted landmark location at the coarsest scale-level for each of the plurality of anatomical landmarks; enforcing spatial coherence of the detected anatomical landmarks by fitting a learned shape model of the plurality of anatomical landmarks to the detected anatomical landmarks at the coarsest scale-level to robustly determine a set of the anatomical landmarks within a field-of-view of the medical image; and refining the detected landmark location for each of the landmarks in the set of anatomical landmarks at each remaining scale-level of the discrete scale-space representation of the medical image using, for each landmark in the set of anatomical landmarks, a respective trained search model trained to predict a trajectory to a predicted landmark location at each remaining scale-level, wherein the trained search model for each remaining scale-level for each landmark is constrained based on a range surrounding the predicted landmark location for that landmark at a previous scale-level, characterized in that for each anatomical landmark, the trained search model for each scale-level is an intelligent artificial agent that predicts the trajectory to the predicted landmark location at the corresponding scale-level by iteratively using a trained deep neural network that inputs a region of interest surrounding a current location in the corresponding scale-level of the discrete scale-space representation of the medical image to calculate action-values corresponding to actions that move the current location in different direction and selecting an action having the highest action-value to be applied to the current location.