title: Robust anatomical landmark detection with application to MR brain image registration.
abstract: Comparison of human brain MR images is often challenged by large inter-subject structural variability. To determine correspondences between MR brain images, most existing methods typically perform a local neighborhood search, based on certain morphological features. They are limited in two aspects: (1) pre-defined morphological features often have limited power in characterizing brain structures, thus leading to inaccurate correspondence detection, and (2) correspondence matching is often restricted within local small neighborhoods and fails to cater to images with large anatomical difference. To address these limitations, we propose a novel method to detect distinctive landmarks for effective correspondence matching. Specifically, we first annotate a group of landmarks in a large set of training MR brain images. Then, we use regression forest to simultaneously learn (1) the optimal sets of features to best characterize each landmark and (2) the non-linear mappings from the local patch appearances of image points to their 3D displacements towards each landmark. The learned regression forests are used as landmark detectors to predict the locations of these landmarks in new images. Because each detector is learned based on features that best distinguish the landmark from other points and also landmark detection is performed in the entire image domain, our method can address the limitations in conventional methods. The deformation field estimated based on the alignment of these detected landmarks can then be used as initialization for image registration. Experimental results show that our method is capable of providing good initialization even for the images with large deformation difference, thus improving registration accuracy.
authors.affiliation: Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
authors.forename: Dong
authors.initials: D
authors.lastname: Han
authors.affiliation: Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
authors.forename: Yaozong
authors.initials: Y
authors.lastname: Gao
authors.affiliation: Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
authors.forename: Guorong
authors.initials: G
authors.lastname: Wu
authors.affiliation: Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
authors.forename: Pew-Thian
authors.initials: PT
authors.lastname: Yap
authors.affiliation: Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Department of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea. Electronic address: dgshen@med.unc.edu.
authors.forename: Dinggang
authors.initials: D
authors.lastname: Shen
mesh_terms: D000465:Algorithms; D059925:Anatomic Landmarks / Q000033:anatomy & histology*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique
keywords: Anatomical landmark detection; Brain MRI; Deformable registration; Random forest regression