3D reconstruction of a femoral shape using a parametric model and two 2D fluoroscopic images

Kurazume, Ryo; Nakamura,; Okada, Shima; Satô,; Sugano,; Koyama,; Iwashita,; Hasegawa, M.

doi:10.1109/robot.2007.363928

Cited by 17 publications

(19 citation statements)

References 25 publications

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“…Besides, Our automatic one-stage framework integrates a novel similarity metric adapted for femoral modeling, with implicit distance maps and intensity models to measure support from image data. Note that prior works proposed by [13] and [6] both of which need some manual interaction to determine 2D contours. Therefore, they require user intervention and a suitable user interface which is not practical in an inter-operative context.…”

Section: Discussionmentioning

confidence: 99%

“…In [6], the authors propose to segment the femur contours in the X-ray images through a level set technique in order to build a distance map before proceeding to registration. Feature-based registration techniques rely on identifying specific landmarks [7,9] and thus require the user intervention and a suitable user interface which is not practical an in inter-operative context.…”

Section: Introductionmentioning

confidence: 99%

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3D Model-based Reconstruction of the Proximal Femur from Low-dose Biplanar X-Ray Images

Boussaid¹,

Kadoury²,

Kokkinos³

et al. 2011

Procedings of the British Machine Vision Conference 2011

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The 3D modeling of the proximal femur is a valuable diagnostic tool for orthopedic surgery planning. The use of computed tomography is the most prominent modality to visualize bones both in terms of resolution as well as in term of bone/tissue separation. Towards reducing the impact of radiation to the patient, low-dose X-ray imaging systems have been introduced while still providing partial views with rather low signal-to-noise ratio. In this paper, we focus on automating the 3D proximal femur reconstruction from simultaneously acquired 2D views. A deformable model represented by triangulated mesh surfaces extends to a linear sub-space describing the variations across individuals. Segmentation consists of inferring a global deformation of 3D model followed by a local adaption based on the most prominent combination of the sub-space parameters. The basis of which relies on the minimization of a cost function based on the biplanar projection of this model. To this end, we employ an active region model that aims at optimizing the 3D model parameters such that projection of surface is attracted from edge potentials, while creating an optimal partition between the bone class and the remaining structures. The global parameters of the model and the local ones are optimized through a gradientfree approach. Promising results demonstrate the potentials of our method compared to a supervised reconstruction technique.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

3D Model-based Reconstruction of the Proximal Femur from Low-dose Biplanar X-Ray Images

Boussaid¹,

Kadoury²,

Kokkinos³

et al. 2011

Procedings of the British Machine Vision Conference 2011

View full text Add to dashboard Cite

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“… 3D reconstruction of anatomic structures from sparse patient-specific input data (Barratt et al 2008, Benameur et al 2003, Blanz et al 2004, Fleute et al 1998, Kurazume et al 2009, Rajamani et al 2007, Stindel et al 2002, Zheng et al 2008, Zheng and Schumann 2009. While 3D image datasets (e.g.…”

Section: Statistical Shape Modelsmentioning

confidence: 99%

Patient-Specific Modelling in Orthopedics: From Image to Surgery

Gomes

Cauter

Beule

et al. 2012

Biomedical Imaging and Computational Modeling in Biomechanics

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In orthopedic surgery, to decide upon intervention and how it can be optimized, surgeons usually rely on subjective analysis of medical images of the patient, obtained from computed tomography, magnetic resonance imaging, ultrasound or other techniques. Recent advancements in computational performance, image analysis and in silico modeling techniques have started to revolutionize clinical practice through the development of quantitative tools, including patient-specific models aiming at improving clinical diagnosis and surgical treatment. Anatomical and surgical landmarks as well as features extraction can be automated allowing for the creation of general or patient-specific models based on statistical shape models. Preoperative virtual planning and rapid prototyping tools allow the implementation of customized surgical solutions in real clinical environments. In the present chapter we discuss the applications of some of these techniques in orthopedics and present new computer-aided tools that can take us from image analysis to customized surgical treatment.

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“…This correspondence enables us not only to align the models but also to transfer the properties of one model, such as texture and motion data, to another model. Therefore, the fitting method is useful in a wide range of applications including Eigenspace-based object modeling (Allen et al, 2003;Blanz et al, 1998;Kurazume et al, 2007), texture mapping, 3D object morphing (Blanz et al, 1998) and computer animation (Noh et al, 2001;Sumner et al, 2004).…”

Section: Introductionmentioning

confidence: 99%

Self-organizing Deformable Model : a Method for Projecting a 3D Object Mesh Model onto a Target Surface

Morooka¹,

Nagahashi²

2012

Computer Graphics

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3D reconstruction of a femoral shape using a parametric model and two 2D fluoroscopic images

Cited by 17 publications

References 25 publications

3D Model-based Reconstruction of the Proximal Femur from Low-dose Biplanar X-Ray Images

3D Model-based Reconstruction of the Proximal Femur from Low-dose Biplanar X-Ray Images

Patient-Specific Modelling in Orthopedics: From Image to Surgery

Self-organizing Deformable Model : a Method for Projecting a 3D Object Mesh Model onto a Target Surface

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