Primal/dual linear programming and statistical atlases for cartilage segmentation.
In this paper we propose a novel approach for automatic segmentation of cartilage using a statistical atlas and efficient primal/dual linear programming. To this end, a novel statistical atlas construction is considered from registered training examples. Segmentation is then solved through registration which aims at deforming the atlas such that the conditional posterior of the learned (atlas) density is maximized with respect to the image. Such a task is reformulated using a discrete set of deformations and segmentation becomes equivalent to finding the set of local deformations which optimally match the model to the image. We evaluate our method on 56 MRI data sets (28 used for the model and 28 used for evaluation) and obtain a fully automatic segmentation of patella cartilage volume with an overlap ratio of 0.84 with a sensitivity and specificity of 94.06% and 99.92%, respectively.
Computer Aided Medical Procedures (CAMP) Technische Universität München. glocker@in.tum.de
Ben
B
Glocker
Nikos
N
Komodakis
Nikos
N
Paragios
Christian
C
Glaser
Georgios
G
Tziritas
Nassir
N
Navab
D000465:Algorithms; D001185:Artificial Intelligence; D002358:Cartilage, Articular / Q000033:anatomy & histology*; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008954:Models, Biological; D015233:Models, Statistical; D017847:Patellar Ligament / Q000033:anatomy & histology*; D010363:Pattern Recognition, Automated / Q000379:methods*; D011382:Programming, Linear; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique