Quantification of articular cartilage from MR images using active shape models

Solloway, Stuart; Taylor, Christopher J.; Hutchinson, Charles E.; Waterton, John C.

doi:10.1007/3-540-61123-1_156

Cited by 19 publications

(6 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Various methods have been proposed for cartilage segmentation [11] [12]. Our method segments medial tibial cartilage in three sequential steps: cartilage edge identification, tibia evolution, and cartilage labeling.…”

Section: Image Analysesmentioning

confidence: 99%

In Vivo MRI Assessment of Knee Cartilage in the Medial Meniscal Tear Model of Osteoarthritis in Rats

Xie

Liachenko

Chiao

et al. 2010

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2010

View full text Add to dashboard Cite

Abstract. We present a new approach for quantifying the degradation of knee cartilage in the medial meniscal tear (MMT) model of osteoarthritis in the rat. A statistical strategy was used to guide the selection of a region of interest (ROI) from the images obtained from a pilot study. We hypothesize that this strategy can be used to localize a region of cartilage most vulnerable to MMT-induced damage. In order to test this hypothesis, a longitudinal study was conducted in which knee cartilage thickness in a pre-selected ROI was monitored for three weeks and comparisons were made between MMT and control rats. We observed a significant decrease in cartilage thickness in MMT rats and a significant increase in cartilage thickness in sham-operated rats as early as one week post surgery when compared to pre-surgery measurements.

show abstract

Section: Image Analysesmentioning

confidence: 99%

In Vivo MRI Assessment of Knee Cartilage in the Medial Meniscal Tear Model of Osteoarthritis in Rats

Xie

Liachenko

Chiao

et al. 2010

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2010

View full text Add to dashboard Cite

show abstract

“…A single transverse slice was segmented, chosen with reference to an atlas of anatomy [15] and corresponded to an anatomic location 6.3 mm posterior to the bregma. Knee Cartilage 15 T1-weighted MR images of the femoral articular cartilage [17]. A single sagital slice was chosen from the centre of the lateral femoral condyles.…”

Section: Resultsmentioning

confidence: 99%

An Efficient Method for Constructing Optimal Statistical Shape Models

Davies¹,

Cootes²,

Waterton

et al. 2001

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2001

Self Cite

View full text Add to dashboard Cite

Statistical shape models show considerable promise as a basis for segmenting and interpreting images. A major drawback of the approach is the need to establish a dense correspondence across a training set of segmented shapes. By posing the problem as one of minimising the description length of the model, we develop an efficient method that automatically defines a correspondence across a set of shapes. As the correspondence does not use an explicit ordering constraint, it generalises to 3D shapes. Results are given for several different training sets of 2D boundaries, showing the automatic method constructs better models than ones built by hand.

show abstract

“…For instance, Solloway reported cartilage segmentation based on ASM [30], [31]. Kapur adopted the strategy to first segment the more easily identifiable femur and tibia bones by region growing, and then detect cartilage voxels using a Bayesian classifier [32].…”

Section: Discussionmentioning

confidence: 99%

LOGISMOS—Layered Optimal Graph Image Segmentation of Multiple Objects and Surfaces: Cartilage Segmentation in the Knee Joint

Yin

Zhang

Williams

et al. 2010

IEEE Trans. Med. Imaging

193

View full text Add to dashboard Cite

A novel method for simultaneous segmentation of multiple interacting surfaces belonging to multiple interacting objects, called LOGISMOS (layered optimal graph image segmentation of multiple objects and surfaces), is reported. The approach is based on the algorithmic incorporation of multiple spatial inter-relationships in a single n-dimensional graph, followed by graph optimization that yields a globally optimal solution. The LOGISMOS method’s utility and performance are demonstrated on a bone and cartilage segmentation task in the human knee joint. Although trained on only a relatively small number of nine example images, this system achieved good performance. Judged by dice similarity coefficients (DSC) using a leave-one-out test, DSC values of 0.84 ± 0.04, 0.80 ± 0.04 and 0.80 ± 0.04 were obtained for the femoral, tibial, and patellar cartilage regions, respectively. These are excellent DSC values, considering the narrow-sheet character of the cartilage regions. Similarly, low signed mean cartilage thickness errors were obtained when compared to a manually-traced independent standard in 60 randomly selected 3-D MR image datasets from the Osteoarthritis Initiative database—0.11 ± 0.24, 0.05 ± 0.23, and 0.03 ± 0.17 mm for the femoral, tibial, and patellar cartilage thickness, respectively. The average signed surface positioning errors for the six detected surfaces ranged from 0.04 ± 0.12 mm to 0.16 ± 0.22 mm. The reported LOGISMOS framework provides robust and accurate segmentation of the knee joint bone and cartilage surfaces of the femur, tibia, and patella. As a general segmentation tool, the developed framework can be applied to a broad range of multiobject multisurface segmentation problems.

show abstract

Quantification of articular cartilage from MR images using active shape models

Cited by 19 publications

References 12 publications

In Vivo MRI Assessment of Knee Cartilage in the Medial Meniscal Tear Model of Osteoarthritis in Rats

In Vivo MRI Assessment of Knee Cartilage in the Medial Meniscal Tear Model of Osteoarthritis in Rats

An Efficient Method for Constructing Optimal Statistical Shape Models

LOGISMOS—Layered Optimal Graph Image Segmentation of Multiple Objects and Surfaces: Cartilage Segmentation in the Knee Joint

Contact Info

Product

Resources

About