Femur shape prediction by multiple regression based on quadric surface fitting

Sholukha, Victor; Chapman, Tara; Salvia, Patrick; Moiseev, Fédor; Euran, Francois; Rooze, Marcel; Jan, Serge Van Sint

doi:10.1016/j.jbiomech.2010.10.039

Cited by 35 publications

(17 citation statements)

References 46 publications

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“…At the distal femur the differences observed among JCSs were small (<5° in the 93% of estimations) but not negligible, therefore the choice of the algorithm, e.g. fitting ellipsoids (Sholukha et al, 2011) or spheres (Yin et al, 2015) to the femoral condyles articular surface, must be justified with careful functional anatomy considerations relevant to the research question. At the proximal tibia, the mechanical axis (Y axis) was similar between Kai-Tibia and GIBOC algorithms (range: 0.9°-2.9°) but less for Miranda-Tibia (up to 7°).…”

Section: Discussionmentioning

confidence: 99%

Automatic Generation of Personalised Skeletal Models of the Lower Limb from Three-Dimensional Bone Geometries

Modenese

Renault

2020

Preprint

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The generation of personalised and patient-specific musculoskeletal models is currently a cumbersome and time-consuming task that normally requires several processing hours and trained operators. We believe that this aspect discourages the use of computational models even when appropriate data are available and personalised biomechanical analysis would be beneficial. In this paper we present a computational tool that enables the fully automatic generation of skeletal models of the lower limb from three-dimensional bone geometries, normally obtained by segmentation of medical images. This tool was validated against four manually created lower limb models finding remarkable agreement in the computed joint parameters, well within human operator repeatability, with the only exception being the subtalar joint axis estimation, which was sensitive to the bone segmentation quality. To prove the robustness of the methodology, the models were built from datasets including both genders, anatomies ranging from juvenile to elderly and bone geometries reconstructed from high-quality computed tomography as well as lower-quality magnetic resonance imaging scans. The entire workflow, implemented in MATLAB scripting language, executed in seconds and required no operator intervention, creating lower extremity models ready to use for kinematic and kinetic analysis or as baselines for more advanced musculoskeletal modelling approaches, of which we provide some practical examples. We auspicate that this technical advancement will promote the use of personalised models in larger-scale studies than those hitherto undertaken.

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Section: Discussionmentioning

confidence: 99%

Automatic Generation of Personalised Skeletal Models of the Lower Limb from Three-Dimensional Bone Geometries

Modenese

Renault

2020

Preprint

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“…which are generally known and presented in [6][7][8][9][10][11][12]. Accurate 3D models of human bones are in the majority of cases created on the basis of the geometrical data acquired from the three-dimensional medical scanning devices (like Computer Tomography CT, 3D Ultrasound), or on the basis of two or more 2D images from two-dimensional scanning devices (X-ray or Ultrasound) [8][9][10][11][12][13]. Possible shortcoming of this approach is the inability to create a model of complete bone in cases of missing bone data.…”

Section: Introductionmentioning

confidence: 99%

“…Geometric entities of predictive models are described by parametric functions, whose arguments are morphometric parameters that can be acquired and measured from medical images. In order to create such models various statistical and numerical methods can be used as described in [7,[10][11][12]. Morphometric parameters are dimensional values which are defined in field of medical morphometrics and they are essential for the successful application of predictive models.…”

Section: Introductionmentioning

confidence: 99%

3d point cloud model of human bio form created by the application of geometric morphometrics and method of anatomical features: human tibia example

Vitković

Radovic

Trajanović

et al. 2019

Filomat

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Morphometrics refers to the quantitative analysis of a biological form and it can be used to describe its shape. Common types of geometric morphometrics are Landmark-based Geometric Morphometrics which describe shape by using anatomical landmarks (e.g. points), and Outline-based geometric morphometrics which uses envelope curves to describe shape of the biological form (e.g. bone), and they are not absolutely exclusive. Geometric morphometrics can be used for the creation of statistical models which represent shape variation of specific bio form. In this paper, novel application of geometric morphometrics for the creation of personalized models of unique bio-forms, i.e. models which are created for the specific patient is presented. Personalized model is defined as 3D point cloud model of biological form (in this case human tibia). Positions of points in 3D space are determined by using set of parametric functions defined by applying geometrical morphometrics, morphology properties and statistical analysis on the input set of human tibia samples. By using this technique, anatomically correct and geometrically accurate personalized models of bio forms can be created and used in pre, intra, and post-operative procedures in clinical practice.

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“…Predictive methods enable the creation of bone geometrical models by using various types of parametric (statistical) models. These methods can provide valid geometrical models, but they are limited by the input set of the bone samples, type of the applied method, and by the number and type of the parameters involved [15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Geometrical Models of Mandible Fracture and Plate Implant

Husain¹,

Rashid²,

Vitković³

et al. 2018

FU Mech Eng

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In the oral and maxillofacial surgery, there is a requirement to provide the best possible treatment for the patient with mandibular fractures. This treatment presumes application of reduction and fixation techniques for proper stabilization of the fracture site. The reduction of the bone fragments and their fixation is much better performed when geometry and morphology of the bone and osteofixation elements (e.g. plates) are properly defined. In this paper, a new healthcare procedure, which enables application of personalized plate implants for the fixation of the mandibular fractures, is presented. Geometrical models of mandible and plate implants, presented in this research, were created by means of the Method of Anatomical Features (MAF), which has been already applied to the creation of accurate geometrical models of various human bones, plates and fixators. By using such geometrically and anatomically accurate models, orthopedic and maxillofacial surgeons can better perform pre-operative tasks of simulating and planning the operation, as well as an intraoperative task of implanting the personalized plate into the patient body.

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Femur shape prediction by multiple regression based on quadric surface fitting

Cited by 35 publications

References 46 publications

Automatic Generation of Personalised Skeletal Models of the Lower Limb from Three-Dimensional Bone Geometries

Automatic Generation of Personalised Skeletal Models of the Lower Limb from Three-Dimensional Bone Geometries

3d point cloud model of human bio form created by the application of geometric morphometrics and method of anatomical features: human tibia example

Geometrical Models of Mandible Fracture and Plate Implant

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