905 resultados para automated registration


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Locally affine (polyaffine) image registration methods capture intersubject non-linear deformations with a low number of parameters, while providing an intuitive interpretation for clinicians. Considering the mandible bone, anatomical shape differences can be found at different scales, e.g. left or right side, teeth, etc. Classically, sequential coarse to fine registration are used to handle multiscale deformations, instead we propose a simultaneous optimization of all scales. To avoid local minima we incorporate a prior on the polyaffine transformations. This kind of groupwise registration approach is natural in a polyaffine context, if we assume one configuration of regions that describes an entire group of images, with varying transformations for each region. In this paper, we reformulate polyaffine deformations in a generative statistical model, which enables us to incorporate deformation statistics as a prior in a Bayesian setting. We find optimal transformations by optimizing the maximum a posteriori probability. We assume that the polyaffine transformations follow a normal distribution with mean and concentration matrix. Parameters of the prior are estimated from an initial coarse to fine registration. Knowing the region structure, we develop a blockwise pseudoinverse to obtain the concentration matrix. To our knowledge, we are the first to introduce simultaneous multiscale optimization through groupwise polyaffine registration. We show results on 42 mandible CT images.

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Placing portal incisions during arthroscopic hip surgery presents challenges for surgeons in terms of anatomic accessibility and patient safety. Based on key anatomic landmarks and portal placement information from recent literature, suggested portal incisions were determined. Guidance in the placement of the three most common portal incision locations (anterior, anterolateral, and posterolateral) for arthroscopic surgery; in addition to visual feedback on tool trajectory to the hip joint is provided in real time by a computer aided system for hip arthroscopy. By simplifying the portal placement process, one of the most challenging aspects of arthroscopic hip surgery, an increased use of this minimally invasive technique could be possible. In addition to portal information, improvements to an existing computer aided system for arthroscopic hip surgery, including a new hip model and redesigned mechanical tracking linkage, were completed.

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PURPOSE : For the facilitation of minimally invasive robotically performed direct cochlea access (DCA) procedure, a surgical planning tool which enables the surgeon to define landmarks for patient-to-image registration, identify the necessary anatomical structures and define a safe DCA trajectory using patient image data (typically computed tomography (CT) or cone beam CT) is required. To this end, a dedicated end-to-end software planning system for the planning of DCA procedures that addresses current deficiencies has been developed. METHODS :    Efficient and robust anatomical segmentation is achieved through the implementation of semiautomatic algorithms; high-accuracy patient-to-image registration is achieved via an automated model-based fiducial detection algorithm and functionality for the interactive definition of a safe drilling trajectory based on case-specific drill positioning uncertainty calculations was developed. RESULTS :    The accuracy and safety of the presented software tool were validated during the conduction of eight DCA procedures performed on cadaver heads. The plan for each ear was completed in less than 20 min, and no damage to vital structures occurred during the procedures. The integrated fiducial detection functionality enabled final positioning accuracies of [Formula: see text] mm. CONCLUSIONS :    Results of this study demonstrated that the proposed software system could aid in the safe planning of a DCA tunnel within an acceptable time.

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MRI-based medical image analysis for brain tumor studies is gaining attention in recent times due to an increased need for efficient and objective evaluation of large amounts of data. While the pioneering approaches applying automated methods for the analysis of brain tumor images date back almost two decades, the current methods are becoming more mature and coming closer to routine clinical application. This review aims to provide a comprehensive overview by giving a brief introduction to brain tumors and imaging of brain tumors first. Then, we review the state of the art in segmentation, registration and modeling related to tumor-bearing brain images with a focus on gliomas. The objective in the segmentation is outlining the tumor including its sub-compartments and surrounding tissues, while the main challenge in registration and modeling is the handling of morphological changes caused by the tumor. The qualities of different approaches are discussed with a focus on methods that can be applied on standard clinical imaging protocols. Finally, a critical assessment of the current state is performed and future developments and trends are addressed, giving special attention to recent developments in radiological tumor assessment guidelines.

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2D-3D registration of pre-operative 3D volumetric data with a series of calibrated and undistorted intra-operative 2D projection images has shown great potential in CT-based surgical navigation because it obviates the invasive procedure of the conventional registration methods. In this study, a recently introduced spline-based multi-resolution 2D-3D image registration algorithm has been adapted together with a novel least-squares normalized pattern intensity (LSNPI) similarity measure for image guided minimally invasive spine surgery. A phantom and a cadaver together with their respective ground truths were specially designed to experimentally assess possible factors that may affect the robustness, accuracy, or efficiency of the registration. Our experiments have shown that it is feasible for the assessed 2D-3D registration algorithm to achieve sub-millimeter accuracy in a realistic setup in less than one minute.

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BACKGROUND: In contrast to RIA, recently available ELISAs provide the potential for fully automated analysis of adiponectin. To date, studies reporting on the diagnostic characteristics of ELISAs and investigating on the relationship between ELISA- and RIA-based methods are rare. METHODS: Thus, we established and evaluated a fully automated platform (BEP 2000; Dade-Behring, Switzerland) for determination of adiponectin levels in serum by two different ELISA methods (competitive human adiponectin ELISA; high sensitivity human adiponectin sandwich ELISA; both Biovendor, Czech Republic). Further, as a reference method, we also employed a human adiponectin RIA (Linco Research, USA). Samples from 150 patients routinely presenting to our cardiology unit were tested. RESULTS: ELISA measurements could be accomplished in less than 3 h, measurement of RIA had a duration of 24 h. The ELISAs were evaluated for precision, analytical sensitivity and specificity, linearity on dilution and spiking recovery. In the investigated patients, type 2 diabetes, higher age and male gender were significantly associated with lower serum adiponectin concentrations. Correlations between the ELISA methods and the RIA were strong (competitive ELISA, r=0.82; sandwich ELISA, r=0.92; both p<0.001). However, Deming regression and Bland-Altman analysis indicated lack of agreement of the 3 methods preventing direct comparison of results. The equations of the regression lines are: Competitive ELISA=1.48 x RIA-0.88; High sensitivity sandwich ELISA=0.77 x RIA+1.01. CONCLUSIONS: Fully automated measurement of adiponectin by ELISA is feasible and substantially more rapid than RIA. The investigated ELISA test systems seem to exhibit analytical characteristics allowing for clinical application. In addition, there is a strong correlation between the ELISA methods and RIA. These findings might promote a more widespread use of adiponectin measurements in clinical research.