title: Keypoint Transfer Segmentation.
abstract: We present an image segmentation method that transfers label maps of entire organs from the training images to the novel image to be segmented. The transfer is based on sparse correspondences between keypoints that represent automatically identified distinctive image locations. Our segmentation algorithm consists of three steps: (i) keypoint matching, (ii) voting-based keypoint labeling, and (iii) keypoint-based probabilistic transfer of organ label maps. We introduce generative models for the inference of keypoint labels and for image segmentation, where keypoint matches are treated as a latent random variable and are marginalized out as part of the algorithm. We report segmentation results for abdominal organs in whole-body CT and in contrast-enhanced CT images. The accuracy of our method compares favorably to common multi-atlas segmentation while offering a speed-up of about three orders of magnitude. Furthermore, keypoint transfer requires no training phase or registration to an atlas. The algorithm's robustness enables the segmentation of scans with highly variable field-of-view.
authors.forename: C
authors.initials: C
authors.lastname: Wachinger
authors.forename: M
authors.initials: M
authors.lastname: Toews
authors.forename: G
authors.initials: G
authors.lastname: Langs
authors.forename: W
authors.initials: W
authors.lastname: Wells
authors.forename: P
authors.initials: P
authors.lastname: Golland
mesh_terms: D000465:Algorithms; D059925:Anatomic Landmarks / Q000000981:diagnostic imaging*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D011860:Radiography, Abdominal / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique; D014057:Tomography, X-Ray Computed / Q000379:methods; D014781:Viscera / Q000000981:diagnostic imaging*; D051598:Whole Body Imaging / Q000379:methods