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

Fripp, Jurgen, Warfield, Stuart, Crozier, Simon K. and Ourselin, Sebastien (2007). Automatic segmentation of articular cartilage in magnetic resonance images of the knee. In: N. Ayache, S. Ourselin and A. Maeder, Medical Image Computing and Computer-Assisted Intervention - MICCAI 2007. 10th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI 2007), Brisbane, Australia, (186-194). 29 October - 2 November 2007. doi:10.1007/978-3-540-75759-7_23

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Author Fripp, Jurgen
Warfield, Stuart
Crozier, Simon K.
Ourselin, Sebastien
Title of paper Automatic segmentation of articular cartilage in magnetic resonance images of the knee
Conference name 10th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI 2007)
Conference location Brisbane, Australia
Conference dates 29 October - 2 November 2007
Proceedings title Medical Image Computing and Computer-Assisted Intervention - MICCAI 2007   Check publisher's open access policy
Journal name Lecture Notes in Computer Science   Check publisher's open access policy
Place of Publication Berlin, Germany
Publisher Springer-Verlag
Publication Year 2007
Sub-type Fully published paper
DOI 10.1007/978-3-540-75759-7_23
ISBN 9783540757580
ISSN 0302-9743
1611-3349
Editor N. Ayache
S. Ourselin
A. Maeder
Volume 4792
Start page 186
End page 194
Total pages 9
Collection year 2008
Language eng
Abstract/Summary 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.
Subjects 291500 Biomedical Engineering
671402 Medical instrumentation
E1
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

 
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Created: Thu, 24 Apr 2008, 12:59:30 EST by Donna Clark on behalf of School of Information Technol and Elec Engineering