AUTOMATIC MULTI-ATLAS-BASED CARTILAGE SEGMENTATION FROM KNEE MR IMAGES.
In this paper, we propose a multi-atlas-based method to automatically segment the femoral and tibial cartilage from T1 weighted magnetic resonance (MR) knee images. The segmentation result is a joint decision of the spatial priors from a multi-atlas registration and the local likelihoods within a Bayesian framework. The cartilage likelihoods are obtained from a probabilisticknearest neighbor classification. Validation results on 18 knee MR images against the 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 75.2% and 81.7% respectively).
Department of Computer Science, University of North Carolina, Chapel Hill, NC, USA.
Liang
L
Shan
Department of Radiology, Duke University, Durham, NC, USA.
Cecil
C
Charles
Department of Computer Science, University of North Carolina, Chapel Hill, NC, USA.
Marc
M
Niethammer
MR; Multi-atlas; bone; cartilage; knee; probabilistic k nearest neighbor; registration; segmentation