title: Anatomical landmark detection using nearest neighbor matching and submodular optimization.
abstract: We present a two-stage method for effective and efficient detection of one or multiple anatomical landmarks in an arbitrary 3D volume. The first stage of nearest neighbor matching is to roughly estimate the landmark locations. It searches out of 100,000 volumes for the closest to an input volume and then transfers landmark annotations to the input. The second stage of submodular optimization is to refine the landmark locations by running discriminative landmark detectors within the search ranges constrained by the first stage results. Further it coordinates multiple detectors with a search strategy optimized on the fly to reduce the overall computation cost arising in a submodular formulation. We validate the accuracy, speed and robustness of our approach by detecting body regions and landmarks in a dataset of 2500 CT scans.
authors.affiliation: Siemens Corporation, Corporate Research and Technology, Princeton, NJ, USA.
authors.forename: David
authors.initials: D
authors.lastname: Liu
authors.forename: S Kevin
authors.initials: SK
authors.lastname: Zhou
mesh_terms: D000465:Algorithms; D059925:Anatomic Landmarks; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity