title: Automatic Atlas-based Three-label Cartilage Segmentation from MR Knee Images.
abstract: This paper proposes a method to build a bone-cartilage atlas of the knee and to use it to automatically segment femoral and tibial cartilage from T1 weighted magnetic resonance (MR) images. Anisotropic spatial regularization is incorporated into a three-label segmentation framework to improve segmentation results for the thin cartilage layers. We jointly use the atlas information and the output of a probabilisticknearest neighbor classifier within the segmentation method. The resulting cartilage segmentation method is fully automatic. Validation results on 18 knee MR images against manual expert segmentations from a dataset acquired for osteoarthritis research show good performance for the segmentation of femoral and tibial cartilage (mean Dice similarity coefficient of 78.2% and 82.6% respectively).
authors.affiliation: Department of Computer Science, UNC Chapel Hill, shan@cs.unc.edu.
authors.forename: Liang
authors.initials: L
authors.lastname: Shan
authors.forename: Cecil
authors.initials: C
authors.lastname: Charles
authors.forename: Marc
authors.initials: M
authors.lastname: Niethammer