Segmenting articular cartilage automatically using a voxel classification approach

TY - JOUR

T1 - Segmenting articular cartilage automatically using a voxel classification approach

AU - Folkesson, Jenny

AU - Dam, Erik B

AU - Olsen, Ole F

AU - Pettersen, Paola C

AU - Christiansen, Claus

PY - 2007/1

Y1 - 2007/1

N2 - We present a fully automatic method for articular cartilage segmentation from magnetic resonance imaging (MRI) which we use as the foundation of a quantitative cartilage assessment. We evaluate our method by comparisons to manual segmentations by a radiologist and by examining the interscan reproducibility of the volume and area estimates. Training and evaluation of the method is performed on a data set consisting of 139 scans of knees with a status ranging from healthy to severely osteoarthritic. This is, to our knowledge, the only fully automatic cartilage segmentation method that has good agreement with manual segmentations, an interscan reproducibility as good as that of a human expert, and enables the separation between healthy and osteoarthritic populations. While high-field scanners offer high-quality imaging from which the articular cartilage have been evaluated extensively using manual and automated image analysis techniques, low-field scanners on the other hand produce lower quality images but to a fraction of the cost of their high-field counterpart. For low-field MRI, there is no well-established accuracy validation for quantitative cartilage estimates, but we show that differences between healthy and osteoarthritic populations are statistically significant using our cartilage volume and surface area estimates, which suggests that low-field MRI analysis can become a useful, affordable tool in clinical studies.

AB - We present a fully automatic method for articular cartilage segmentation from magnetic resonance imaging (MRI) which we use as the foundation of a quantitative cartilage assessment. We evaluate our method by comparisons to manual segmentations by a radiologist and by examining the interscan reproducibility of the volume and area estimates. Training and evaluation of the method is performed on a data set consisting of 139 scans of knees with a status ranging from healthy to severely osteoarthritic. This is, to our knowledge, the only fully automatic cartilage segmentation method that has good agreement with manual segmentations, an interscan reproducibility as good as that of a human expert, and enables the separation between healthy and osteoarthritic populations. While high-field scanners offer high-quality imaging from which the articular cartilage have been evaluated extensively using manual and automated image analysis techniques, low-field scanners on the other hand produce lower quality images but to a fraction of the cost of their high-field counterpart. For low-field MRI, there is no well-established accuracy validation for quantitative cartilage estimates, but we show that differences between healthy and osteoarthritic populations are statistically significant using our cartilage volume and surface area estimates, which suggests that low-field MRI analysis can become a useful, affordable tool in clinical studies.

KW - Adult

KW - Aged

KW - Algorithms

KW - Artificial Intelligence

KW - Cartilage, Articular

KW - Cluster Analysis

KW - Female

KW - Humans

KW - Image Enhancement

KW - Image Interpretation, Computer-Assisted

KW - Imaging, Three-Dimensional

KW - Information Storage and Retrieval

KW - Male

KW - Middle Aged

KW - Osteoarthritis

KW - Pattern Recognition, Automated

KW - Reproducibility of Results

KW - Sensitivity and Specificity

KW - Signal Processing, Computer-Assisted

KW - Evaluation Studies

KW - Journal Article

U2 - 10.1109/TMI.2006.886808

DO - 10.1109/TMI.2006.886808

M3 - Journal article

C2 - 17243589

SN - 0278-0062

VL - 26

SP - 106

EP - 115

JO - IEEE Transactions on Medical Imaging

JF - IEEE Transactions on Medical Imaging

IS - 1

ER -