11 March 2008 Automatic knee cartilage delineation using inheritable segmentation
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We present a fully automatic method for segmentation of knee joint cartilage from fat suppressed MRI. The method first applies 3-D model-based segmentation technology, which allows to reliably segment the femur, patella, and tibia by iterative adaptation of the model according to image gradients. Thin plate spline interpolation is used in the next step to position deformable cartilage models for each of the three bones with reference to the segmented bone models. After initialization, the cartilage models are fine adjusted by automatic iterative adaptation to image data based on gray value gradients. The method has been validated on a collection of 8 (3 left, 5 right) fat suppressed datasets and demonstrated the sensitivity of 83±6% compared to manual segmentation on a per voxel basis as primary endpoint. Gross cartilage volume measurement yielded an average error of 9±7% as secondary endpoint. For cartilage being a thin structure, already small deviations in distance result in large errors on a per voxel basis, rendering the primary endpoint a hard criterion.
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Sebastian P. M. Dries, Vladimir Pekar, Daniel Bystrov, Harald S. Heese, Thomas Blaffert, Clemens Bos, Arianne M. C. van Muiswinkel, "Automatic knee cartilage delineation using inheritable segmentation", Proc. SPIE 6914, Medical Imaging 2008: Image Processing, 691439 (11 March 2008); doi: 10.1117/12.769189; https://doi.org/10.1117/12.769189

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