Spatially consistent multi-scale anatomical landmark detection in incomplete 3D-CT data

An apparatus for automated spatially-consistent detection of anatomical landmarks in a medical image of a patient, comprising:

• means for 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;
• means for 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;
• means for 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
• means for refining the predicted 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 the 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.

A non-transitory computer readable medium storing computer program instructions for automated spatially-consistent detection of anatomical landmarks in a medical image of a patient, the computer program instructions when executed by a processor cause the processor to perform operations 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 predicted 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 the 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.