Development of Semi-Automatic Segmentation Methods for Measuring Tibial Cartilage Volume

2006 ◽  
Author(s):  
J. Cheong ◽  
D. Suter ◽  
F. Cicuttini
Keyword(s):  
Automatic Segmentation ◽  
Cartilage Volume ◽  
Tibial Cartilage ◽  
Segmentation Methods

Assessment of automatic segmentation accuracy with various point cloud density

2020 ◽  
Vol 961 (7) ◽  
pp. 47-55
Author(s):  
A.G. Yunusov ◽  
A.J. Jdeed ◽  
N.S. Begliarov ◽  
M.A. Elshewy
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Reference Data ◽  
Automatic Segmentation ◽  
Geometric Accuracy ◽  
Segmentation Method ◽  
Segmentation Methods ◽  
Segmentation Accuracy ◽  
Number Of Segments ◽  
Cloud Density

Laser scanning is considered as one of the most useful and fast technologies for modelling. On the other hand, the size of scan results can vary from hundreds to several million points. As a result, the large volume of the obtained clouds leads to complication at processing the results and increases the time costs. One way to reduce the volume of a point cloud is segmentation, which reduces the amount of data from several million points to a limited number of segments. In this article, we evaluated effect on the performance, the accuracy of various segmentation methods and the geometric accuracy of the obtained models at density changes taking into account the processing time. The results of our experiment were compared with reference data in a form of comparative analysis. As a conclusion, some recommendations for choosing the best segmentation method were proposed.

scholarly journals Weight change and change in tibial cartilage volume and symptoms in obese adults

2014 ◽  
Vol 22 ◽  
pp. S358
Author(s):  
A. Teichtahl ◽  
A. Wluka ◽  
S. Tanamas ◽  
Y. Wang ◽  
B. Strauss ◽  
...  
Keyword(s):  
Weight Change ◽  
Cartilage Volume ◽  
Obese Adults ◽  
Tibial Cartilage

Higher dynamic medial knee load predicts greater cartilage loss over 12 months in medial knee osteoarthritis

2011 ◽  
Vol 70 (10) ◽  
pp. 1770-1774 ◽  
Author(s):  
Kim L Bennell ◽  
Kelly-Ann Bowles ◽  
Yuanyuan Wang ◽  
Flavia Cicuttini ◽  
Miranda Davies-Tuck ◽  
...  
Keyword(s):  
Knee Osteoarthritis ◽  
Disease Progression ◽  
Cartilage Volume ◽  
Cartilage Defects ◽  
Medial Knee ◽  
Tibial Cartilage ◽  
Medial Knee Osteoarthritis ◽  
Structural Progression ◽  
The Relationship ◽  
Knee Load

ObjectiveMechanical factors, in particular increased medial knee joint load, are believed to be important in the structural progression of knee osteoarthritis. This study evaluated the relationship of medial knee load during walking to indices of structural disease progression, measured on MRI, in people with medial knee osteoarthritis.MethodsA longitudinal cohort design utilising a subset of participants (n=144, 72%) enrolled in a randomised controlled trial of lateral wedge insoles was employed. Medial knee load parameters including the peak knee adduction moment (KAM) and the KAM impulse were measured at baseline using three-dimensional gait analysis during walking. MRI at baseline and at 12 months was used to assess structural indices. Multiple regression with adjustment for covariates assessed the relationship between medial knee load parameters and the annual change in medial tibial cartilage volume. Binary logistic regression was used for the dichotomous variables of progression of medial tibiofemoral cartilage defects and bone marrow lesions (BML).ResultsA higher KAM impulse, but not peak KAM, at baseline was independently associated with greater loss of medial tibial cartilage volume over 12 months (β=29.9, 95% CI 6.3 to 53.5, p=0.01). No significant relationships were seen between medial knee load parameters and the progression of medial tibiofemoral cartilage defects or BML.ConclusionThis study suggests knee loading, in particular the KAM impulse, may be a risk factor for loss of medial tibial cartilage volume. As knee load is modifiable, load-modifying treatments may potentially slow disease progression.

Association between metabolic syndrome and knee structural change on MRI

Rheumatology ◽  
2019 ◽  
Author(s):  
Feng Pan ◽  
Jing Tian ◽  
Siti Maisarah Mattap ◽  
Flavia Cicuttini ◽  
Graeme Jones
Keyword(s):  
Metabolic Syndrome ◽  
Relative Risk ◽  
Central Obesity ◽  
Cartilage Volume ◽  
Size Increase ◽  
Volume Loss ◽  
Tibial Cartilage ◽  
Knee Oa ◽  
Low Hdl ◽  
Cartilage Volume Loss

Abstract Objective To examine the association of metabolic syndrome (MetS) and its components with knee cartilage volume loss and bone marrow lesion (BML) change. Methods Longitudinal data on 435 participants from a population-based cohort study were analysed. Blood pressure, glucose, triglycerides and high-density lipoprotein (HDL) were collected. MetS was defined based on the National Cholesterol Education Program–Adult Treatment Panel III criteria. MRI of the right knee was performed to measure cartilage volume and BML. Radiographic knee OA was assessed by X-ray and graded using the Altman atlas for osteophytes and joint space narrowing. Results Thirty-two percent of participants had MetS and 60% had radiographic knee OA. In multivariable analysis, the following were independently associated with medial tibial cartilage volume loss: MetS, β = −0.30%; central obesity, β = −0.26%; and low HDL, β = −0.25% per annum. MetS, hypertriglyceridaemia and low HDL were also associated with higher risk of BML size increase in the medial compartment (MetS: relative risk 1.72, 95% CI 1.22, 2.43; hypertriglyceridaemia: relative risk 1.43, 95% CI 1.01, 2.02; low HDL: relative risk 1.67, 95% CI 1.18, 2.36). After further adjustment for central obesity or BMI, MetS and low HDL remained statistically significant for medial tibial cartilage volume loss and BML size increase. The number of components of MetS correlated with greater cartilage volume loss and BML size increase (both P for trend <0.05). There were no statistically significant associations in the lateral compartment. Conclusion MetS and low HDL are associated with medial compartment cartilage volume loss and BML size increase, suggesting that targeting these factors has the potential to prevent or slow knee structural change.

Effect of Intra-articular Platelet-Rich Plasma vs Placebo Injection on Pain and Medial Tibial Cartilage Volume in Patients With Knee Osteoarthritis

JAMA ◽  
2021 ◽  
Vol 326 (20) ◽  
pp. 2021
Author(s):  
Kim L. Bennell ◽  
Kade L. Paterson ◽  
Ben R. Metcalf ◽  
Vicky Duong ◽  
Jillian Eyles ◽  
...  
Keyword(s):  
Knee Osteoarthritis ◽  
Platelet Rich Plasma ◽  
Cartilage Volume ◽  
Tibial Cartilage

Automatic Correction of Gaps in Cerebrovascular Segmentations Extracted from 3D Time-of-Flight MRA Datasets

2012 ◽  
Vol 51 (05) ◽  
pp. 415-422 ◽  
Author(s):  
A. Schmidt-Richberg ◽  
J. Fiehler ◽  
T. Illies ◽  
D. Möller ◽  
H. Handels ◽  
...  
Keyword(s):  
Level Set ◽  
Automatic Segmentation ◽  
Time Of Flight ◽  
Medical Expert ◽  
Consistency Check ◽  
Major Drawback ◽  
Segmentation Methods ◽  
Initial Segmentation ◽  
Level Set Approach ◽  
Manual Correction

Summary Objectives: Exact cerebrovascular segmentations are required for several applications in today’s clinical routine. A major drawback of typical automatic segmentation methods is the occurrence of gaps within the segmentation. These gaps are typically located at small vessel structures exhibiting low intensities. Manual correction is very time-consuming and not suitable in clinical practice. This work presents a post-processing method for the automatic detection and closing of gaps in cerebrovascular segmentations. Methods: In this approach, the 3D centerline is calculated from an available vessel segmentation, which enables the detection of corresponding vessel endpoints. These endpoints are then used to detect possible connections to other 3D centerline voxels with a graph-based approach. After consistency check, reasonable detected paths are expanded to the vessel boundaries using a level set approach and combined with the initial segmentation. Results: For evaluation purposes, 100 gaps were artificially inserted at non-branching vessels and bifurcations in manual cerebrovascular segmentations derived from ten Time-of-Flight magnetic resonance angiography datasets. The results show that the presented method is capable of detecting 82% of the non-branching vessel gaps and 84% of the bifurcation gaps. The level set segmentation expands the detected connections with 0.42 mm accuracy compared to the initial segmentations. A further evaluation based on 10 real automatic segmentations from the same datasets shows that the proposed method detects 35 additional connections in average per dataset, whereas 92.7% were rated as correct by a medical expert. Conclusion: The presented approach can considerably improve the accuracy of cerebrovascular segmentations and of following analysis outcomes.

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