Cartilage segmentation of 3D MRI scans of the osteoarthritic knee combining user knowledge and active contours

John Andrew Lynch; Souhil Zaim; Jenny Zhao; Alexander Stork; Charles G. Peterfy; Harry K. Genant

doi:10.1117/12.387758

6 June 2000 Cartilage segmentation of 3D MRI scans of the osteoarthritic knee combining user knowledge and active contours

John Andrew Lynch, Souhil Zaim, Jenny Zhao, Alexander Stork, Charles G. Peterfy, Harry K. Genant

Author Affiliations +

Proceedings Volume 3979, Medical Imaging 2000: Image Processing; (2000) https://doi.org/10.1117/12.387758
Event: Medical Imaging 2000, 2000, San Diego, CA, United States

Abstract

A technique for segmentation of articular cartilage from 3D MRI scans of the knee has been developed. It overcomes the limitations of the conventionally used region growing techniques, which are prone to inter- and intra-observer variability, and which can require much manual intervention. We describe a hybrid segmentation method combining expert knowledge with directionally oriented Canny filters, cost functions and cubic splines. After manual initialization, the technique utilized 3 cost functions which aided automated detection of cartilage and its boundaries. Using the sign of the edge strength, and the local direction of the boundary, this technique is more reliable than conventional 'snakes,' and the user had little control over smoothness of boundaries. This means that the automatically detected boundary can conform to the true shape of the real boundary, also allowing reliable detection of subtle local lesions on the normally smooth cartilage surface. Manual corrections, with possible re-optimization were sometimes needed. When compared to the conventionally used region growing techniques, this newly described technique measured local cartilage volume with 3 times better reproducibility, and involved two thirds less human interaction. Combined with the use of 3D image registration, the new technique should also permit unbiased segmentation of followup scans by automated initialization from a baseline segmentation of an earlier scan of the same patient.

Citation Download Citation

John Andrew Lynch, Souhil Zaim, Jenny Zhao, Alexander Stork, Charles G. Peterfy, and Harry K. Genant "Cartilage segmentation of 3D MRI scans of the osteoarthritic knee combining user knowledge and active contours", Proc. SPIE 3979, Medical Imaging 2000: Image Processing, (6 June 2000); https://doi.org/10.1117/12.387758

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