Automated segmentation of the menisci from MR images

Authors:
Jurgen Fripp;Pierrick Bourgeat;Craig Engstrom;Sébastien Ourselin;Stuart Crozier;Olivier Salvado
Affiliations:
The Australian e-Health Research Centre, BioMedIA, CSIRO, ICTC, Herston, Australia;The Australian e-Health Research Centre, BioMedIA, CSIRO, ICTC, Herston, Australia;School of ITEE, University of Queensland, Brisbane, Australia;Centre for Medical Image Computing, University College London, London, United Kingdom;School of ITEE, University of Queensland, Brisbane, Australia;The Australian e-Health Research Centre, BioMedIA, CSIRO, ICTC, Herston, Australia
Venue:
ISBI'09 Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro
Year:
2009

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Abstract

Pathologic processes active in early-stage knee joint osteoarthritis may also affect the integrity of the crescentshaped fibrocartilagenous structures calledmenisci. Magnetic resonance imaging can allow the detection of these structural changes, however, large-scale clinical application remains limited by tedious and labor-intensive techniques for volumetric measurement. Towards automating these quantitative measurements, we have currently developed a scheme that allows the automatic segmentation of the menisci from MR images of healthy knees. This scheme utilizes prior automatic bone and cartilage segmentations to provide spatial localization, before shape model fitting and tissue classification are used to segment the menisci. The accuracy and robustness of the approach was experimentally validated using a set of 14 fat suppressed Spoiled Gradient Recall MR images. An average Dice Similarity Coefficient of 0.75 and 0.77 was obtained for the medial and lateral meniscus, illustrating the accuracy of the approach, while the coefficient of variation for volume was 2.29 and 1.50, respectively.