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This study aimed at developing a new automatic segmentation algorithm for human knee cartilage volume quantification from magnetic resonance images (MRI). Imaging was performed using a 3T scanner and a knee coil, and the exam consisted of a DESS sequence which contrasts cartilage and soft tissues including the synovial fluid. The algorithm was developed on MRI 3D 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. Firstly, the MR images are resampled in the neighborhood of the bone surface. Secondly, 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. Thirdly, 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, median DSC=0.84), for the femur (r=0.95, p<0.0001, median DSC=0.85) and the tibia (r=0.83, p<0.0001, 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, p=0.016) as well as for the femur (r=0.79, p=0.011). The test-retest revealed 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.
This article was published in the following journal.
Name: IEEE transactions on bio-medical engineering
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