Active Shape Models-Their Training and Application

Cootes, Timothy F.; Taylor, Chris; Cooper, D. H.; Graham, James

doi:10.1006/cviu.1995.1004

Cited by 6,331 publications

(4,327 citation statements)

References 13 publications

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“…An SSM allows the quantitative description of the total shape of an object and all possible variation that exists in a study population. We created an SSM of the shape of the hip in anteroposterior radiographs, using the freely available Active Shape Model toolkit (Manchester University, Manchester, UK) (10). The model consisted of 70 landmark points on contours along the femur, acetabulum, and pelvis.…”

Section: Methodsmentioning

confidence: 99%

Osteoarthritis susceptibility genes influence the association between hip morphology and osteoarthritis

Waarsing

Kloppenburg

Slagboom

et al. 2011

Arthritis & Rheumatism

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Objective. The identified osteoarthritis (OA) susceptibility genes are mainly active in skeletal development and could thus affect joint geometry. Because nonoptimal joint geometry is a risk factor for the development of OA, we investigated if and how the path that leads from nonoptimal joint geometry to OA of the hip is influenced by these genes.Methods. The shape of the hips of subjects in the Genetics, Osteoarthritis and Progression Study, consisting of sibling pairs with symptomatic OA at multiple joint locations, was quantified by applying a statistical shape model to radiographs. Shape aspects (modes) were correlated to OA characteristics. We then tested for the association of shape modes with OA susceptibility single-nucleotide polymorphisms (SNPs) of GDF5, FRZB, and DIO2.Results. Four of 23 shape modes (mode 1, mode 17, mode 18, and mode 21) were strongly associated with OA characteristics. We observed a significant interaction between carrier status of DIO2 rs12885300 and hip OA characteristics for mode 1 (P ‫؍‬ 0.005). This indicates that this specific aspect of hip shape correlates with OA characteristics only in carriers of the susceptibility allele.Conclusion. Our results suggest that it is more likely that the rs12885300 SNP of DIO2 increases the vulnerability of cartilage to nonoptimal bone shapes rather than directly influencing the formation of these shapes.The genetic predisposition for osteoarthritis (OA) (1) may reflect the result of multiple interacting genes with small effects (2). The identification of such genes should be considered essential to obtain a better understanding of OA and the underlying biologic events preceding its onset (3). One path leading to OA in which genes might play a role starts with the causal effect of hip morphology on the development of OA and may explain part of the heritability of hip OA. The life-long biomechanical stress caused by a nonoptimal shape of the bones in the joint leads to recurrent damage of cartilage, which eventually triggers the onset of OA (4).What is interesting in this respect is the fact that genes that are mainly active in skeletal development, such as FRZB and GDF5, are identified as OA susceptibility genes (5,6). These genes are involved in the orchestration of growth plate chondrocytes that results in formation of cartilage and eventually bone during endochondral ossification (7). A recent large-scale metaanalysis (6) did not provide evidence for the relatively rare FRZB OA risk alleles, indicating that these variants do not confer susceptibility to common OA. It is, however, generally known that meta-analyses have little power to detect relatively rare variants with a modest effect that confer risk in a specific subset of OA cases. Furthermore, functional studies in Frzb-knockout mice showed increased cortical bone thickness and density, resulting in stiffer bones upon mechanical loading, which may increase OA susceptibility and stresses developmental aspects (8).

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Section: Methodsmentioning

confidence: 99%

Osteoarthritis susceptibility genes influence the association between hip morphology and osteoarthritis

Waarsing

Kloppenburg

Slagboom

et al. 2011

Arthritis & Rheumatism

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“…We have also shown (see Equation (16)) that morphology-based strategies can also be employed to devise effective affinities. It is also conceivable that affinities can be constructed by utilizing information available in statistical shape models [28]. A question naturally arises then as to whether these three strategies can be combined in a FC-driven segmentation task to construct affinities.…”

Section: Resultsmentioning

confidence: 99%

Iterative relative fuzzy connectedness for multiple objects with multiple seeds

Ciesielski

Udupa

Saha

et al. 2007

Computer Vision and Image Understanding

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In this paper we present a new theory and an algorithm for image segmentation based on a strength of connectedness between every pair of image elements. The object definition used in the segmentation algorithm utilizes the notion of iterative relative fuzzy connectedness, IRFC. In previously published research, the IRFC theory was developed only for the case when the segmentation was involved with just two segments, an object and a background, and each of the segments was indicated by a single seed. (See Udupa, Saha, Lotufo [15] and Saha, Udupa [14].) Our theory, which solves a problem of Udupa and Saha from [13], allows simultaneous segmentation involving an arbitrary number of objects. Moreover, each segment can be indicated by more than one seed, which is often more natural and easier than a single seed object identification.The first iteration step of the IRFC algorithm gives a segmentation known as relative fuzzy connectedness, RFC, segmentation. Thus, the IRFC technique is an extension of the RFC method. Although the RFC theory, due to Saha and Udupa [19], is developed in the multi object/multi seed framework, the theoretical results presented here are considerably more delicate in nature and do not use the results from [19]. On the other hand, the theoretical results from [19] are immediate consequences of the results presented here. Moreover, the new framework not only subsumes previous fuzzy connectedness descriptions but also sheds new light on them. Thus, there are fundamental theoretical advances made in this paper.We present examples of segmentations obtained via our IRFC based algorithm in the multi object/ multi seed environment, and compare it with the results obtained with the RFC based algorithm. Our results indicate that, in many situations, IRFC outperforms RFC, but there also exist instances where the gain in performance is negligible.

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“…From each volume, we extracted the femur's triangulated surface with 2562 points and 5120 facets using the marching cubes technique [19]. To create the SSM, corresponding points on different shapes have to be determined [20] [21]. In our case, corresponding points between the shapes were selected using the automatic algorithm described in [18].…”

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confidence: 99%

Fluoroscopy-based tracking of femoral kinematics with statistical shape models

Valenti

Momi

et al. 2015

Int J CARS

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Abstract:Purpose Precise knee kinematics assessment helps to diagnose knee pathologies and to improve the design of customized prosthetic components. The first step in identifying knee kinematics is to assess the femoral motion in the anatomical frame. However, no work has been done on pathological femurs, whose shape can be highly different from healthy ones. Method We propose a new femoral tracking technique based on Statistical Shape Models (SSMs) and two calibrated fluoroscopic images, taken at different flexion-extension angles. The cost function optimization is based on genetic algorithms, to avoid local minima. The proposed approach was evaluated on 3 sets of digitally reconstructed radiographic images of osteoarthritic patients. Results It is found that using the estimated shape, rather than that calculated from CT, significantly reduces the pose accuracy, but still has reasonably good results (angle errors around 2 degrees, translation around 1.5mm). Cover Letter. Powered by Editorial Manager® and ProduXion Manager® from Aries Systems CorporationThe authors thank the reviewers and the editor for their interest in this topic and for giving them the possibility to improve the paper with their comments. Hereunder we answer the questions arisen, with indications on the changes made to the manuscript According to the reviewer comment, we modified the text in the "State of the Art" section as follows:"Knee kinematics assessment has great importance both to understand the problems associated with a large number of knee pathologies and to improve the design of prosthetic components. In case of severe osteoarthritis, that are eligible for joint implant surgery, in vivo pre-operatory knee kinematics is fundamental to understand the relative motion between the three joint bones. The relative movement can give an insight of how the ligaments are stretched and their stability, and how could be the feeling for the patient. In this way, pre operatory knee kinematics could help the surgeon to decide which prosthesis should be used and how to correct the misalignment of the bones [3]. Currently, the reconstruction of the pose of the knee can be done using 3D scans such as real-time Magnetic Resonance Imaging (MRI) or through a 2D/3D registration method that superimposes the shape extracted from MRI or Computed Tomography (CT) onto an image, usually X-ray or fluoroscopy. Real-time MRI is suitable to study joint kinematics, as it evidences the muscle structure during movements. However, it can only be used with relatively slow movements, and the accuracy obtained increases from 1mm to more than 3mm depending on the velocity of the movement. In addition, MRI scans are highly expensive [4]. Traditional CT and MRI provide an accurate evaluation of the morphology of the knee, but are limited to static positioning of the patient.[…]The use of dynamic fluoroscopy to detect knee kinematics is described in [12]. The authors use a fluoroscopic system flashing at 30Hz, obtaining continuous images of the knee flexion from 0° to 120°...

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Active Shape Models-Their Training and Application

Cited by 6,331 publications

References 13 publications

Osteoarthritis susceptibility genes influence the association between hip morphology and osteoarthritis

Osteoarthritis susceptibility genes influence the association between hip morphology and osteoarthritis

Iterative relative fuzzy connectedness for multiple objects with multiple seeds

Fluoroscopy-based tracking of femoral kinematics with statistical shape models

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