Instantiation and registration of statistical shape models of the femur and pelvis using 3D ultrasound imaging

Barratt, Dean C.; Chan, Carolyn S. K.; Edwards, Philip; Penney, Graeme P.; Slomczykowski, M.; Carter, Timothy J.; Hawkes, David J.

doi:10.1016/j.media.2007.12.006

Cited by 134 publications

(88 citation statements)

References 41 publications

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“…The approach of concentrating on landmark-based shape representation and variants of Active Shape [14] and Active Appearance [13] models are known as Statistic shape models (SSM) [26]. Comparing with the first method, SSM could prevent the risks correlated with CT scans [6], largely save the computational cost for the sake of fewer image preprocessing steps involved, and the final output model could be put into the pre-model database for the future application. However, studies in SSM field are still immature and need further validation, so this review mainly discusses the patient-specific modeling through the medical image processing.…”

Section: Geometry and Materials Properties Acquisitionmentioning

confidence: 99%

Virtual Reality Surgery Simulation: A Survey on Patient Specific Solution

Zhang

Chang

Yang

et al. 2017

Next Generation Computer Animation Techniques

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Abstract. For surgeons, the precise anatomy structure and its dynamics are important in the surgery interaction, which is critical for generating the immersive experience in VR based surgical training applications. Presently, a normal therapeutic scheme might not be able to be straightforwardly applied to a specific patient, because the diagnostic results are based on averages, which result in a rough solution. Patient Specific Modeling (PSM), using patient-specific medical image data (e.g. CT, MRI, or Ultrasound), could deliver a computational anatomical model. It provides the potential for surgeons to practice the operation procedures for a particular patient, which will improve the accuracy of diagnosis and treatment, thus enhance the prophetic ability of VR simulation framework and raise the patient care. This paper presents a general review based on existing literature of patient specific surgical simulation on data acquisition, medical image segmentation, computational mesh generation, and soft tissue real time simulation.

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Section: Geometry and Materials Properties Acquisitionmentioning

confidence: 99%

Virtual Reality Surgery Simulation: A Survey on Patient Specific Solution

Zhang

Chang

Yang

et al. 2017

Next Generation Computer Animation Techniques

View full text Add to dashboard Cite

show abstract

“… 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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“…Bone surfaces have to be detected in order to refine the registration with the medical images space or in order to morph a statistical model of the bone [4][5][6] in order to make it patient specific, so to accurately plan the best choice for the implant size.…”

Section: Introductionmentioning

confidence: 99%

“…Detected surfaces are referenced to reference frames fixed to the patient 10,11 . Recently, US images (B-mode) were used for initializing and registering statistical shape models of the femur 5 and pelvis 4 .…”

Section: Introductionmentioning

confidence: 99%

Experimental validation of A-mode ultrasound acquisition system for computer assisted orthopaedic surgery

et al. 2009

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Computer Assisted Orthopaedic Surgery (CAOS) systems improve the results and the standardization of surgical interventions. Anatomical landmarks and bone surface detection is straightforward to either register the surgical space with the pre-operative imaging space and to compute biomechanical parameters for prosthesis alignment.Surface points acquisition increases the intervention invasiveness and can be influenced by the soft tissue layer interposition (7-15mm localization errors).This study is aimed at evaluating the accuracy of a custom-made A-mode ultrasound (US) system for non invasive detection of anatomical landmarks and surfaces. A-mode solutions eliminate the necessity of US images segmentation, offers real-time signal processing and requires less invasive equipment. The system consists in a single transducer US probe optically tracked, a pulser/receiver and an FPGA-based board, which is responsible for logic control command generation and for real-time signal processing and three custom-made board (signal acquisition, blanking and synchronization).We propose a new calibration method of the US system. The experimental validation was then performed measuring the length of known-shape polymethylmethacrylate boxes filled with pure water and acquiring bone surface points on a bovine bone phantom covered with soft-tissue mimicking materials. Measurement errors were computed through MR and CT images acquisitions of the phantom. Points acquisition on bone surface with the US system demonstrated lower errors (1.2mm) than standard pointer acquisition (4.2mm).

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Instantiation and registration of statistical shape models of the femur and pelvis using 3D ultrasound imaging

Cited by 134 publications

References 41 publications

Virtual Reality Surgery Simulation: A Survey on Patient Specific Solution

Virtual Reality Surgery Simulation: A Survey on Patient Specific Solution

Patient-Specific Modelling in Orthopedics: From Image to Surgery

Experimental validation of A-mode ultrasound acquisition system for computer assisted orthopaedic surgery

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