title: Fully Automatic Localization and Segmentation of 3D Vertebral Bodies from CT/MR Images via a Learning-Based Method.
abstract: In this paper, we address the problems of fully automatic localization and segmentation of 3D vertebral bodies from CT/MR images. We propose a learning-based, unified random forest regression and classification framework to tackle these two problems. More specifically, in the first stage, the localization of 3D vertebral bodies is solved with random forest regression where we aggregate the votes from a set of randomly sampled image patches to get a probability map of the center of a target vertebral body in a given image. The resultant probability map is then further regularized by Hidden Markov Model (HMM) to eliminate potential ambiguity caused by the neighboring vertebral bodies. The output from the first stage allows us to define a region of interest (ROI) for the segmentation step, where we use random forest classification to estimate the likelihood of a voxel in the ROI being foreground or background. The estimated likelihood is combined with the prior probability, which is learned from a set of training data, to get the posterior probability of the voxel. The segmentation of the target vertebral body is then done by a binary thresholding of the estimated probability. We evaluated the present approach on two openly available datasets: 1) 3D T2-weighted spine MR images from 23 patients and 2) 3D spine CT images from 10 patients. Taking manual segmentation as the ground truth (each MR image contains at least 7 vertebral bodies from T11 to L5 and each CT image contains 5 vertebral bodies from L1 to L5), we evaluated the present approach with leave-one-out experiments. Specifically, for the T2-weighted MR images, we achieved for localization a mean error of 1.6 mm, and for segmentation a mean Dice metric of 88.7% and a mean surface distance of 1.5 mm, respectively. For the CT images we achieved for localization a mean error of 1.9 mm, and for segmentation a mean Dice metric of 91.0% and a mean surface distance of 0.9 mm, respectively.
authors.affiliation: Institution for Surgical Technology and Biomechanics, University of Bern, 3014 Bern, Switzerland.
authors.forename: Chengwen
authors.initials: C
authors.lastname: Chu
authors.affiliation: Charité - University Medicine Berlin, Centre of Muscle and Bone Research, Campus Benjamin Franklin, Free University & Humboldt-University Berlin, 12200 Berlin, Germany.|Centre for Physical Activity and Nutrition Research, School of Exercise and Nutrition Sciences, Deakin University Burwood Campus, Burwood VIC 3125, Australia.
authors.forename: Daniel L
authors.initials: DL
authors.lastname: Belavý
authors.affiliation: Centre for Physical Activity and Nutrition Research, School of Exercise and Nutrition Sciences, Deakin University Burwood Campus, Burwood VIC 3125, Australia.
authors.forename: Gabriele
authors.initials: G
authors.lastname: Armbrecht
authors.affiliation: Institut für Diagnostische und Interventionelle Radiologie, Krankenhaus Porz Am Rhein gGmbH, 51149 Köln, Germany.
authors.forename: Martin
authors.initials: M
authors.lastname: Bansmann
authors.affiliation: Centre for Physical Activity and Nutrition Research, School of Exercise and Nutrition Sciences, Deakin University Burwood Campus, Burwood VIC 3125, Australia.
authors.forename: Dieter
authors.initials: D
authors.lastname: Felsenberg
authors.affiliation: Institution for Surgical Technology and Biomechanics, University of Bern, 3014 Bern, Switzerland.
authors.forename: Guoyan
authors.initials: G
authors.lastname: Zheng
mesh_terms: D000465:Algorithms; D006801:Humans; D021621:Imaging, Three-Dimensional; D008279:Magnetic Resonance Imaging; D013131:Spine / Q000000981:diagnostic imaging* / Q000473:pathology*; D014057:Tomography, X-Ray Computed