title: Global localization of 3D anatomical structures by pre-filtered Hough forests and discrete optimization.
abstract: The accurate localization of anatomical landmarks is a challenging task, often solved by domain specific approaches. We propose a method for the automatic localization of landmarks in complex, repetitive anatomical structures. The key idea is to combine three steps: (1) a classifier for pre-filtering anatomical landmark positions that (2) are refined through a Hough regression model, together with (3) a parts-based model of the global landmark topology to select the final landmark positions. During training landmarks are annotated in a set of example volumes. A classifier learns local landmark appearance, and Hough regressors are trained to aggregate neighborhood information to a precise landmark coordinate position. A non-parametric geometric model encodes the spatial relationships between the landmarks and derives a topology which connects mutually predictive landmarks. During the global search we classify all voxels in the query volume, and perform regression-based agglomeration of landmark probabilities to highly accurate and specific candidate points at potential landmark locations. We encode the candidates' weights together with the conformity of the connecting edges to the learnt geometric model in a Markov Random Field (MRF). By solving the corresponding discrete optimization problem, the most probable location for each model landmark is found in the query volume. We show that this approach is able to consistently localize the model landmarks despite the complex and repetitive character of the anatomical structures on three challenging data sets (hand radiographs, hand CTs, and whole body CTs), with a median localization error of 0.80 mm, 1.19 mm and 2.71 mm, respectively.
authors.affiliation: Computational Image Analysis and Radiology Lab, Department of Radiology, Medical University of Vienna, Austria; Institute for Computer Graphics and Vision, Graz University of Technology, Austria. Electronic address: rene.donner@meduniwien.ac.at.
authors.forename: René
authors.initials: R
authors.lastname: Donner
authors.forename: Bjoern H
authors.initials: BH
authors.lastname: Menze
authors.forename: Horst
authors.initials: H
authors.lastname: Bischof
authors.forename: Georg
authors.initials: G
authors.lastname: Langs
mesh_terms: D000465:Algorithms; D059925:Anatomic Landmarks / Q000000981:diagnostic imaging*; D003627:Data Interpretation, Statistical; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D012044:Regression Analysis; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014057:Tomography, X-Ray Computed / Q000379:methods*; D051598:Whole Body Imaging / Q000379:methods*
keywords: Anatomical structure localization; Hough regression; Interest point detection; Random Forests