title: Personalization of pictorial structures for anatomical landmark localization.
abstract: We propose a method for accurately localizing anatomical landmarks in 3D medical volumes based on dense matching of parts-based graphical models. Our novel approach replaces population mean models by jointly leveraging weighted combinations of labeled exemplars (both spatial and appearance) to obtain personalized models for the localization of arbitrary landmarks in upper body images. We compare the method to a baseline population-mean graphical model and atlas-based deformable registration optimized for CT-CT registration, by measuring the localization accuracy of 22 anatomical landmarks in clinical 3D CT volumes, using a database of 83 lung cancer patients. The average mean localization error across all landmarks is 2.35 voxels. Our proposed method outperforms deformable registration by 73%, 93% for the most improved landmark. Compared to the baseline population-mean graphical model, the average improvement of localization accuracy is 32%; 67% for the most improved landmark.
authors.affiliation: Department of Engineering Science, University of Oxford.
authors.forename: Vaclav
authors.initials: V
authors.lastname: Potesil
authors.forename: Timor
authors.initials: T
authors.lastname: Kadir
authors.forename: Günther
authors.initials: G
authors.lastname: Platsch
authors.forename: Sir Michael
authors.initials: SM
authors.lastname: Brady
mesh_terms: D000465:Algorithms; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods*; D008175:Lung Neoplasms / Q000000981:diagnostic imaging*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique; D014057:Tomography, X-Ray Computed / Q000379:methods*