DocumentCode :
1535807
Title :
Automatic Human Knee Cartilage Segmentation From 3-D Magnetic Resonance Images
Author :
Dodin, Pierre ; Pelletier, Jean-Pierre ; Martel-Pelletier, Johanne ; Abram, François
Author_Institution :
ArthroVision Inc., Montreal, QC, Canada
Volume :
57
Issue :
11
fYear :
2010
Firstpage :
2699
Lastpage :
2711
Abstract :
This study aimed at developing a new automatic segmentation algorithm for human knee cartilage volume quantification from MRI. Imaging was performed using a 3T scanner and a knee coil, and the exam consisted of a double echo steady state (DESS) sequence, which contrasts cartilage and soft tissues including the synovial fluid. The algorithm was developed on MRI 3-D images in which the bone-cartilage interface for the femur and tibia was segmented by an independent segmentation process, giving a parametric surface of the interface. First, the MR images are resampled in the neighborhood of the bone surface. Second, by using texture-analysis techniques optimized by filtering, the cartilage is discriminated as a bright and homogeneous tissue. This process of excluding soft tissues enables the detection of the external boundary of the cartilage. Third, a technology based on a Bayesian decision criterion enables the automatic separation of the cartilage and synovial fluid. Finally, the cartilage volume and changes in volume for an individual between visits was assessed using the developed technology. Validation included first, for nine knee osteoarthritis patients, a comparison of the cartilage volume and changes over time between the developed automatic system and a validated semi-automatic cartilage volume system, and second, for five knee osteoarthritis patients, a test-retest procedure. Data revealed excellent Pearson correlations and Dice similarity coefficients (DSC) for the global knee (r = 0.96 , p <; 0.0001, and median DSC = 0.84), for the femur ( r = 0.95, p <; 0.0001, and median DSC = 0.85 ), and the tibia (r = 0.83, p <; 0.0001, and median DSC = 0.84). Very good similarity between the automatic and semi-automatic methods in regard to cartilage loss was also found for the global knee (r = 0.76 and p = 0.016) as well as for the femur (r = 0.79 and p = 0.011). The test-retest reveale- - d an excellent measurement error of -0.3 ±1.6% for the global knee and 0.14 ±1.7% for the femur. In conclusion, the newly developed fully automatic method described herein provides accurate and precise quantification of knee cartilage volume and will be a valuable tool for clinical follow-up studies.
Keywords :
Bayes methods; biological tissues; biomedical MRI; diseases; image sampling; image segmentation; image texture; medical image processing; 3D magnetic resonance image; 3T scanner; Bayesian decision criterion; Dice similarity coefficient; MRI; Pearson correlation; automatic human knee cartilage segmentation; automatic segmentation algorithm; bone-cartilage interface; cartilage volume; double echo steady state sequence; femur; human knee cartilage volume quantification; knee coil; knee osteoarthritis patient; soft tissue; synovial fluid; test-retest procedure; texture-analysis; tibia; Cartilage volume; image resampling; magnetic resonance imaging (MRI); surface parameterization; texture analysis; three-dimensional segmentation;
fLanguage :
English
Journal_Title :
Biomedical Engineering, IEEE Transactions on
Publisher :
ieee
ISSN :
0018-9294
Type :
jour
DOI :
10.1109/TBME.2010.2058112
Filename :
5510111
Link To Document :
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