title: Automatic X-ray landmark detection and shape segmentation via data-driven joint estimation of image displacements.
abstract: In this paper, we propose a new method for fully-automatic landmark detection and shape segmentation in X-ray images. To detect landmarks, we estimate the displacements from some randomly sampled image patches to the (unknown) landmark positions, and then we integrate these predictions via a voting scheme. Our key contribution is a new algorithm for estimating these displacements. Different from other methods where each image patch independently predicts its displacement, we jointly estimate the displacements from all patches together in a data driven way, by considering not only the training data but also geometric constraints on the test image. The displacements estimation is formulated as a convex optimization problem that can be solved efficiently. Finally, we use the sparse shape composition model as the a priori information to regularize the landmark positions and thus generate the segmented shape contour. We validate our method on X-ray image datasets of three different anatomical structures: complete femur, proximal femur and pelvis. Experiments show that our method is accurate and robust in landmark detection, and, combined with the shape model, gives a better or comparable performance in shape segmentation compared to state-of-the art methods. Finally, a preliminary study using CT data shows the extensibility of our method to 3D data.
authors.affiliation: Institute for Surgical Technology and Biomechanics, University of Bern, Stauffacherstr. 78, CH-3014 Bern, Switzerland. Electronic address: cheng.chen@istb.unibe.ch.
authors.forename: C
authors.initials: C
authors.lastname: Chen
authors.affiliation: Institute for Surgical Technology and Biomechanics, University of Bern, Stauffacherstr. 78, CH-3014 Bern, Switzerland.
authors.forename: W
authors.initials: W
authors.lastname: Xie
authors.affiliation: BG Trauma Centre Ludwigshafen at Heidelberg University Hospital, Ludwig-Guttmann-Str. 13, D-67071 Ludwigshafen, Germany.
authors.forename: J
authors.initials: J
authors.lastname: Franke
authors.affiliation: BG Trauma Centre Ludwigshafen at Heidelberg University Hospital, Ludwig-Guttmann-Str. 13, D-67071 Ludwigshafen, Germany.
authors.forename: P A
authors.initials: PA
authors.lastname: Grutzner
authors.affiliation: Institute for Surgical Technology and Biomechanics, University of Bern, Stauffacherstr. 78, CH-3014 Bern, Switzerland.
authors.forename: L-P
authors.initials: LP
authors.lastname: Nolte
authors.affiliation: Institute for Surgical Technology and Biomechanics, University of Bern, Stauffacherstr. 78, CH-3014 Bern, Switzerland. Electronic address: guoyan.zheng@istb.unibe.ch.
authors.forename: G
authors.initials: G
authors.lastname: Zheng
mesh_terms: D000465:Algorithms; D059925:Anatomic Landmarks / Q000000981:diagnostic imaging*; D001185:Artificial Intelligence; D005269:Femur / Q000000981:diagnostic imaging; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D010388:Pelvis / Q000000981:diagnostic imaging; D011856:Radiographic Image Enhancement / Q000379:methods; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014057:Tomography, X-Ray Computed / Q000379:methods*
keywords: Data-driven estimation; Femur; Landmark detection; Shape segmentation; X-ray image