Automatic segmentation of articular cartilage in magnetic resonance images of the knee

Authors:
Jurgen Fripp;Stuart Crozier;Simon K. Warfield;Sébastien Ourselin
Affiliations:
BioMedIA Lab, e-Health Research Centre, CSIRO, ICT Centre, Australia and School of ITEE, University of Queensland, Australia;School of ITEE, University of Queensland, Australia;Computational Radiology Laboratory, Harvard Medical School, Children's Hospital Boston;BioMedIA Lab, e-Health Research Centre, CSIRO, ICT Centre, Australia
Venue:
MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention
Year:
2007

Citing 2
Cited 3

Active shape models—their training and application

Computer Vision and Image Understanding
Simultaneous segmentation of multiple closed surfaces using optimal graph searching

IPMI'05 Proceedings of the 19th international conference on Information Processing in Medical Imaging

Prediction of the Repair Surface over Cartilage Defects: A Comparison of Three Methods in a Sheep Model

MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part I
Automated segmentation of the menisci from MR images

ISBI'09 Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro
In vivo MRI assessment of knee cartilage in the medial meniscal tear model of osteoarthritis in rats

MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part III

Quantified Score

Hi-index	0.00

Visualization

Abstract

To perform cartilage quantitative analysis requires the accurate segmentation of each individual cartilage. In this paper we present a model based scheme that can automatically and accurately segment each individual cartilage in healthy knees from a clinical MR sequence (fat suppressed spoiled gradient recall). This scheme consists of three stages; the automatic segmentation of the bones, the extraction of the bone-cartilage interfaces (BCI) and segmentation of the cartilages. The bone segmentation is performed using three-dimensional active shape models. The BCI is extracted using image information and prior knowledge about the likelihood of each point belonging to the interface. A cartilage thickness model then provides constraints and regularizes the cartilage segmentation performed from the BCI. The accuracy and robustness of the approach was experimentally validated, with (patellar, tibial and femoral) cartilage segmentations having a median DSC of (0.870, 0.855, 0.870), performing significantly better than non-rigid registration (0.787, 0.814, 0.795). The total cartilage segmentation had an average DSC of (0.891), close to the (0.896) obtained using a semi-automatic watershed algorithm. The error in quantitative volume and thickness measures was (8.29, 4.94, 5.56)% and (0.19, 0.33, 0.10) mm respectively.