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Accuracy of model-based RSA contour reduction in a typical clinical application.

ABSTRACT: Marker-based roentgen stereophotogrammetric analysis (RSA) is an accurate method for measuring in vivo implant migration, which requires attachment of tantalum markers to the implant. Model-based RSA allows migration measurement without implant markers; digital pose estimation, which can be thought of as casting a shadow of a surface model of the implant into the stereoradiographs, is used instead. The number of surface models required in a given clinical study depends on the number of implanted sizes and design variations of prostheses. Contour selection can be used to limit pose estimation to areas of the prosthesis that do not vary with design, reducing the number of surface models required. The effect of contour reduction on the accuracy of the model-based method was investigated using three different contour selection schemes on tibial components in 24 patients at 3 and 6 month followup. The agreement interval (mean +/- 2 standard deviations), which bounds the differences between the marker-based and model-based methods with contour reduction was smaller than -0.028 +/- 0.254 mm. The data suggest that contour reduction does not result in unacceptable loss of model-based RSA accuracy, and that the model-based method can be used interchangeably with the marker-based method for measuring tibial component migration.

SUBMITTER: Hurschler C

PROVIDER: S-EPMC2584241 | biostudies-other | 2008 Aug

REPOSITORIES: biostudies-other

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Comparison between model-based RSA and an AI-based CT-RSA: an accuracy study of 30 patients.

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| S-EPMC10824248 | biostudies-literature

Accuracy of an autocalibrated pulse contour analysis in cardiac surgery patients: a bi-center clinical trial.

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Inferring pulmonary exposure based on clinical PK data: accuracy and precision of model-based deconvolution methods.

Project description:Determining and understanding the target-site exposure in clinical studies remains challenging. This is especially true for oral drug inhalation for local treatment, where the target-site is identical to the site of drug absorption, i.e., the lungs. Modeling and simulation based on clinical pharmacokinetic (PK) data may be a valid approach to infer the pulmonary fate of orally inhaled drugs, even without local measurements. In this work, a simulation-estimation study was systematically applied to investigate five published model structures for pulmonary drug absorption. First, these models were compared for structural identifiability and how choosing an inadequate model impacts the inference on pulmonary exposure. Second, in the context of the population approach both sequential and simultaneous parameter estimation methods after intravenous administration and oral inhalation were evaluated with typically applied models. With an adequate model structure and a well-characterized systemic PK after intravenous dosing, the error in inferring pulmonary exposure and retention times was less than twofold in the majority of evaluations. Whether a sequential or simultaneous parameter estimation was applied did not affect the inferred pulmonary PK to a relevant degree. One scenario in the population PK analysis demonstrated biased pulmonary exposure metrics caused by inadequate estimation of systemic PK parameters. Overall, it was demonstrated that empirical modeling of intravenous and inhalation PK datasets provided robust estimates regarding accuracy and bias for the pulmonary exposure and pulmonary retention, even in presence of the high variability after drug inhalation.

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A recurrent neural model for proto-object based contour integration and figure-ground segregation.

Project description:Visual processing of objects makes use of both feedforward and feedback streams of information. However, the nature of feedback signals is largely unknown, as is the identity of the neuronal populations in lower visual areas that receive them. Here, we develop a recurrent neural model to address these questions in the context of contour integration and figure-ground segregation. A key feature of our model is the use of grouping neurons whose activity represents tentative objects ("proto-objects") based on the integration of local feature information. Grouping neurons receive input from an organized set of local feature neurons, and project modulatory feedback to those same neurons. Additionally, inhibition at both the local feature level and the object representation level biases the interpretation of the visual scene in agreement with principles from Gestalt psychology. Our model explains several sets of neurophysiological results (Zhou et al. Journal of Neuroscience, 20(17), 6594-6611 2000; Qiu et al. Nature Neuroscience, 10(11), 1492-1499 2007; Chen et al. Neuron, 82(3), 682-694 2014), and makes testable predictions about the influence of neuronal feedback and attentional selection on neural responses across different visual areas. Our model also provides a framework for understanding how object-based attention is able to select both objects and the features associated with them.

| S-EPMC5693639 | biostudies-literature

A gradient-based, GPU-accelerated, high-precision contour-segmentation algorithm with application to cell membrane fluctuation spectroscopy.

Project description:We present a novel intensity-gradient based algorithm specifically designed for nanometer-segmentation of cell membrane contours obtained with high-resolution optical microscopy combined with high-velocity digital imaging. The algorithm relies on the image oversampling performance and computational power of graphical processing units (GPUs). Both, synthetic and experimental data are used to quantify the sub-pixel precision of the algorithm, whose analytic performance results comparatively higher than in previous methods. Results from the synthetic data indicate that the spatial precision of the presented algorithm is only limited by the signal-to-noise ratio (SNR) of the contour image. We emphasize on the application of the new algorithm to membrane fluctuations (flickering) in eukaryotic cells, bacteria and giant vesicle models. The method shows promising applicability in several fields of cellular biology and medical imaging for nanometer-precise boundary-determination and mechanical fingerprinting of cellular membranes in optical microscopy images. Our implementation of this high-precision flicker spectroscopy contour tracking algorithm (HiPFSTA) is provided as open-source at www.github.com/michaelmell/hipfsta.

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Accuracy Evaluation of CONTOUR(®)PLUS Compared With Four Blood Glucose Monitoring Systems.

Project description:IntroductionThe aim of this study was to compare the accuracy of 5 blood glucose monitoring systems (BGMSs; CONTOUR(®)PLUS [CP], Accu-Chek(®) Active [ACA], Accu-Chek(®) Performa [ACP], FreeStyle Freedom™ [FF], OneTouch(®) SelectSimple™ [OTSS]).MethodsStudy staff tested fingerstick samples from 106 subjects aged ≥18 years using the 5 BGMSs. Some samples were modified to achieve blood glucose concentrations throughout the measuring range. The primary endpoint was comparison of the mean absolute relative difference (MARD) from the reference value (Yellow Springs Instruments [YSI]) across the overall tested glucose range. Other endpoints were MARD in the low (≤80 mg/dL [≤4.4 mmol/L]), middle (81-180 mg/dL [4.5-10.0 mmol/L]), and high (>180 mg/dL [>10.0 mmol/L]) glucose ranges, and MARD for unmodified samples in the overall glucose range.ResultsCONTOUR(®)PLUS had a statistically significantly lower MARD than all BGMSs across the overall tested range (27-460 mg/dL [1.5-25.5 mmol/L]) and in the high glucose range. In the low glucose range, CP had a lower MARD than all BGMSs, which was statistically significant except for ACP. For unmodified samples across the overall tested range, CP had a lower MARD than all BGMSs and was statistically significantly lower except for ACA.ConclusionsCONTOUR(®)PLUS had the lowest mean difference from the reference values (by MARD) when compared with other BGMSs across multiple glucose ranges with modified and unmodified samples.FundingBayer HealthCare LLC, Diabetes Care.Trial registration numberClinicalTrials.gov Identifier NCT01714232.

| S-EPMC4575307 | biostudies-literature

Contour complexity and contour detection.

Project description:Itis well-known that "smooth" chains of oriented elements-contours-are more easily detected amid background noise than more undulating (i.e., "less smooth") chains. Here, we develop a Bayesian framework for contour detection and show that it predicts that contour detection performance should decrease with the contour's complexity, quantified as the description length (DL; i.e., the negative logarithm of probability integrated along the contour). We tested this prediction in two experiments in which subjects were asked to detect simple open contours amid pixel noise. In Experiment 1, we demonstrate a consistent decline in performance with increasingly complex contours, as predicted by the Bayesian model. In Experiment 2, we confirmed that this effect is due to integrated complexity along the contour, and does not seem to depend on local stretches of linear structure. The results corroborate the probabilistic model of contours, and show how contour detection can be understood as a special case of a more general process-the identification of organized patterns in the environment.

| S-EPMC4429927 | biostudies-literature

Brain extraction from cerebral MRI volume using a hybrid level set based active contour neighborhood model.

Project description:BackgroundThe extraction of brain tissue from cerebral MRI volume is an important pre-procedure for neuroimage analyses. The authors have developed an accurate and robust brain extraction method using a hybrid level set based active contour neighborhood model.MethodsThe method uses a nonlinear speed function in the hybrid level set model to eliminate boundary leakage. When using the new hybrid level set model an active contour neighborhood model is applied iteratively in the neighborhood of brain boundary. A slice by slice contour initial method is proposed to obtain the neighborhood of the brain boundary. The method was applied to the internet brain MRI data provided by the Internet Brain Segmentation Repository (IBSR).ResultsIn testing, a mean Dice similarity coefficient of 0.95±0.02 and a mean Hausdorff distance of 12.4±4.5 were obtained when performing our method across the IBSR data set (18 × 1.5 mm scans). The results obtained using our method were very similar to those produced using manual segmentation and achieved the smallest mean Hausdorff distance on the IBSR data.ConclusionsAn automatic method of brain extraction from cerebral MRI volume was achieved and produced competitively accurate results.

| S-EPMC3639852 | biostudies-literature

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