Validation of statistical shape model based reconstruction of the proximal femur - A morphology study

Seventeen bones (sixteen cadaveric bones and one plastic bone) were used to validate a method for reconstructing a surface model of the proximal femur from 2D X-ray radiographs and a statistical shape model that was constructed from thirty training surface models. Unlike previously introduced validation studies, where surface-based distance errors were used to evaluate the reconstruction accuracy, here we propose to use errors measured based on clinically relevant morphometric parameters. For this purpose, a program was developed to robustly extract those morphometric parameters from the thirty training surface models (training population), from the seventeen surface models reconstructed from X-ray radiographs, and from the seventeen ground truth surface models obtained either by a CT-scan reconstruction method or by a laser-scan reconstruction method. A statistical analysis was then performed to classify the seventeen test bones into two categories: normal cases and outliers. This classification step depends on the measured parameters of the particular test bone. In case all parameters of a test bone were covered by the training population's parameter ranges, this bone is classified as normal bone, otherwise as outlier bone. Our experimental results showed that statistically there was no significant difference between the morphometric parameters extracted from the reconstructed surface models of the normal cases and those extracted from the reconstructed surface models of the outliers. Therefore, our statistical shape model based reconstruction technique can be used to reconstruct not only the surface model of a normal bone but also that of an outlier bone.

Is restricted femoral navigation sufficient for accuracy of total knee arthroplasty?

A total knee arthroplasty performed with navigation results in more accurate component positioning with fewer outliers. It is not known whether image-based or image-free-systems are preferable and if navigation for only one component leads to equal accuracy in leg alignment than navigation of both components. We evaluated the results of total knee arthroplasties performed with femoral navigation. We studied 90 knees in 88 patients who had conventional total knee arthroplasties, image-based total knee arthroplasties, or total knee arthroplasties with image-free navigation. We compared patients' perioperative times, component alignment accuracy, and short-term outcomes. The total surgical time was longer in the image-based total knee arthroplasty group (109 +/- 7 minutes) compared with the image-free (101 +/- 17 minutes) and conventional total knee arthroplasty groups (87 +/- 20 minutes). The mechanical axis of the leg was within 3 degrees of neutral alignment, although the conventional total knee arthroplasty group showed more (10.6 degrees ) variance than the navigated groups (5.8 degrees and 6.4 degrees , respectively). We found a positive correlation between femoral component malalignment and the total mechanical axis in the conventional group. Our results suggest image-based navigation is not necessary, and image-free femoral navigation may be sufficient for accurate component alignment.

Accurate and robust reconstruction of proximal femur from sparse intraoperative data and dense point distribution model for surgical navigation

Constructing a 3D surface model from sparse-point data is a nontrivial task. Here, we report an accurate and robust approach for reconstructing a surface model of the proximal femur from sparse-point data and a dense-point distribution model (DPDM). The problem is formulated as a three-stage optimal estimation process. The first stage, affine registration, is to iteratively estimate a scale and a rigid transformation between the mean surface model of the DPDM and the sparse input points. The estimation results of the first stage are used to establish point correspondences for the second stage, statistical instantiation, which stably instantiates a surface model from the DPDM using a statistical approach. This surface model is then fed to the third stage, kernel-based deformation, which further refines the surface model. Handling outliers is achieved by consistently employing the least trimmed squares (LTS) approach with a roughly estimated outlier rate in all three stages. If an optimal value of the outlier rate is preferred, we propose a hypothesis testing procedure to automatically estimate it. We present here our validations using four experiments, which include 1 leave-one-out experiment, 2 experiment on evaluating the present approach for handling pathology, 3 experiment on evaluating the present approach for handling outliers, and 4 experiment on reconstructing surface models of seven dry cadaver femurs using clinically relevant data without noise and with noise added. Our validation results demonstrate the robust performance of the present approach in handling outliers, pathology, and noise. An average 95-percentile error of 1.7-2.3 mm was found when the present approach was used to reconstruct surface models of the cadaver femurs from sparse-point data with noise added.

Unifying energy minimization and mutual information maximization for robust 2D/3D registration of X-ray and CT images

Similarity measure is one of the main factors that affect the accuracy of intensity-based 2D/3D registration of X-ray fluoroscopy to CT images. Information theory has been used to derive similarity measure for image registration leading to the introduction of mutual information, an accurate similarity measure for multi-modal and mono-modal image registration tasks. However, it is known that the standard mutual information measure only takes intensity values into account without considering spatial information and its robustness is questionable. Previous attempt to incorporate spatial information into mutual information either requires computing the entropy of higher dimensional probability distributions, or is not robust to outliers. In this paper, we show how to incorporate spatial information into mutual information without suffering from these problems. Using a variational approximation derived from the Kullback-Leibler bound, spatial information can be effectively incorporated into mutual information via energy minimization. The resulting similarity measure has a least-squares form and can be effectively minimized by a multi-resolution Levenberg-Marquardt optimizer. Experimental results are presented on datasets of two applications: (a) intra-operative patient pose estimation from a few (e.g. 2) calibrated fluoroscopic images, and (b) post-operative cup alignment estimation from single X-ray radiograph with gonadal shielding.

Indocyanine green plasma disappearance rate during the anhepatic phase of orthotopic liver transplantation

Non-invasive pulse spectrophotometry to measure indocyanine green (ICG) elimination correlates well with the conventional invasive ICG clearance test. Nevertheless, the precision of this method remains unclear for any application, including small-for-size liver remnants. We therefore measured ICG plasma disappearance rate (PDR) during the anhepatic phase of orthotopic liver transplantation using pulse spectrophotometry. Measurements were done in 24 patients. The median PDR after exclusion of two outliers and two patients with inconstant signal was 1.55%/min (95% confidence interval [CI]=0.8-2.2). No correlation with patient age, gender, body mass, blood loss, administration of fresh frozen plasma, norepinephrine dose, postoperative albumin (serum), or difference in pre and post transplant body weight was detected. In conclusion, we found an ICG-PDR different from zero in the anhepatic phase, an overestimation that may arise in particular from a redistribution into the interstitial space. If ICG pulse spectrophotometry is used to measure functional hepatic reserve, the verified average difference from zero (1.55%/min) determined in our study needs to be taken into account.

Prediction of soil organic carbon for Ethiopian highlands using soil spectroscopy

Soil spectroscopy was applied for predicting soil organic carbon (SOC) in the highlands of Ethiopia. Soil samples were acquired from Ethiopia’s National Soil Testing Centre and direct field sampling. The reflectance of samples was measured using a FieldSpec 3 diffuse reflectance spectrometer. Outliers and sample relation were evaluated using principal component analysis (PCA) and models were developed through partial least square regression (PLSR). For nine watersheds sampled, 20% of the samples were set aside to test prediction and 80% were used to develop calibration models. Depending on the number of samples per watershed, cross validation or independent validation were used.The stability of models was evaluated using coefficient of determination (R2), root mean square error (RMSE), and the ratio performance deviation (RPD). The R2 (%), RMSE (%), and RPD, respectively, for validation were Anjeni (88, 0.44, 3.05), Bale (86, 0.52, 2.7), Basketo (89, 0.57, 3.0), Benishangul (91, 0.30, 3.4), Kersa (82, 0.44, 2.4), Kola tembien (75, 0.44, 1.9),Maybar (84. 0.57, 2.5),Megech (85, 0.15, 2.6), andWondoGenet (86, 0.52, 2.7) indicating that themodels were stable. Models performed better for areas with high SOC values than areas with lower SOC values. Overall, soil spectroscopy performance ranged from very good to good.

Adaptive evolution during an ongoing range expansion: the invasive bank vole (Myodes glareolus) in Ireland

Range expansions are extremely common, but have only recently begun to attract attention in terms of their genetic consequences. As populations expand, demes at the wave front experience strong genetic drift, which is expected to reduce genetic diversity and potentially cause ‘allele surfing’, where alleles may become fixed over a wide geographical area even if their effects are deleterious. Previous simulation models show that range expansions can generate very strong selective gradients on dispersal, reproduction, competition and immunity. To investigate the effects of range expansion on genetic diversity and adaptation, we studied the population genomics of the bank vole (Myodes glareolus) in Ireland. The bank vole was likely introduced in the late 1920s and is expanding its range at a rate of ~2.5 km/year. Using genotyping-by-sequencing, we genotyped 281 bank voles at 5979 SNP loci. Fourteen sample sites were arranged in three transects running from the introduction site to the wave front of the expansion. We found significant declines in genetic diversity along all three transects. However, there was no evidence that sites at the wave front had accumulated more deleterious mutations. We looked for outlier loci with strong correlations between allele frequency and distance from the introduction site, where the direction of correlation was the same in all three transects. Amongst these outliers, we found significant enrichment for genic SNPs, suggesting the action of selection. Candidates for selection included several genes with immunological functions and several genes that could influence behaviour.

A Closed-Form, Consistent and Robust Solution to Uncalibrated Photometric Stereo Via Local Diffuse Reflectance Maxima

Images of an object under different illumination are known to provide strong cues about the object surface. A mathematical formalization of how to recover the normal map of such a surface leads to the so-called uncalibrated photometric stereo problem. In the simplest instance, this problem can be reduced to the task of identifying only three parameters: the so-called generalized bas-relief (GBR) ambiguity. The challenge is to find additional general assumptions about the object, that identify these parameters uniquely. Current approaches are not consistent, i.e., they provide different solutions when run multiple times on the same data. To address this limitation, we propose exploiting local diffuse reflectance (LDR) maxima, i.e., points in the scene where the normal vector is parallel to the illumination direction (see Fig. 1). We demonstrate several noteworthy properties of these maxima: a closed-form solution, computational efficiency and GBR consistency. An LDR maximum yields a simple closed-form solution corresponding to a semi-circle in the GBR parameters space (see Fig. 2); because as few as two diffuse maxima in different images identify a unique solution, the identification of the GBR parameters can be achieved very efficiently; finally, the algorithm is consistent as it always returns the same solution given the same data. Our algorithm is also remarkably robust: It can obtain an accurate estimate of the GBR parameters even with extremely high levels of outliers in the detected maxima (up to 80 % of the observations). The method is validated on real data and achieves state-of-the-art results.

Improved sampling for gradient-domain metropolis light transport

We present a generalized framework for gradient-domain Metropolis rendering, and introduce three techniques to reduce sampling artifacts and variance. The first one is a heuristic weighting strategy that combines several sampling techniques to avoid outliers. The second one is an improved mapping to generate offset paths required for computing gradients. Here we leverage the properties of manifold walks in path space to cancel out singularities. Finally, the third technique introduces generalized screen space gradient kernels. This approach aligns the gradient kernels with image structures such as texture edges and geometric discontinuities to obtain sparser gradients than with the conventional gradient kernel. We implement our framework on top of an existing Metropolis sampler, and we demonstrate significant improvements in visual and numerical quality of our results compared to previous work.

Eine tragfähige Datenbrücke – Phänologische Beobachtungen im Amtsblatt des Kantons Schaffhausen 1876–1950

The international standardisation of national meteorological networks in the late nineteenth century excluded biotic and abiotic observations from the objects to be henceforth published in the yearbooks. Skilled amateurs being in charge of three meteorological stations in Canton Schaffhausen (Switzerland) and their successors managed to continuously publish phenological observations gathered in the station environment alongside with meteorological data in the official gazette of this Canton from 1876 to 1950, i.e. up to the onset of phenological network observations in Switzerland. At least ten observations are available for 51 plant and animal phenological phases. Long series were assembled (N → = 30) for 14 plant phenological observations, among them for the first flowering of snowdrop (Galanthus nivalis), of hazel (Corylus avellana), of horse chestnut (Aesculus hippocastanum), of winter rye (Secale cereale) and of grape vine (Vitis vinifera) as well as the beginning of hay, winter rye and grape harvesting. Only the bare data were published without any metadata. The quality of 10 long series (N →=60) was checked by investigating the biographical and biological background of key observers and submitting their evidence to graphical (meteorological plausibility check of outliers) and statistical verification. The long term observers, mostly schoolteachers and high school professors, had a good knowledge of botany and the quality of their observations – disregarding obvious printing errors – is surprisingly good. A number of long series (seven) was completed with applicable data from the Swiss Phenological Network up to 2011. Besides anthropogenic shifts (beginning of hay and grape harvest) there is a contrast between a global warming-related earlier flowering of snowdrop and hazel and a later occurrence of grape vine flowering.

Detecting outlying studies in meta-regression models using a forward search algorithm.

When considering data from many trials, it is likely that some of them present a markedly different intervention effect or exert an undue influence on the summary results. We develop a forward search algorithm for identifying outlying and influential studies in meta-analysis models. The forward search algorithm starts by fitting the hypothesized model to a small subset of likely outlier-free studies and proceeds by adding studies into the set one-by-one that are determined to be closest to the fitted model of the existing set. As each study is added to the set, plots of estimated parameters and measures of fit are monitored to identify outliers by sharp changes in the forward plots. We apply the proposed outlier detection method to two real data sets; a meta-analysis of 26 studies that examines the effect of writing-to-learn interventions on academic achievement adjusting for three possible effect modifiers, and a meta-analysis of 70 studies that compares a fluoride toothpaste treatment to placebo for preventing dental caries in children. A simple simulated example is used to illustrate the steps of the proposed methodology, and a small-scale simulation study is conducted to evaluate the performance of the proposed method. Copyright © 2016 John Wiley & Sons, Ltd.

Modern diatom, cladocera, chironomid, and chrysophyte cyst assemblages as quantitative indicators for the reconstruction of past environmental conditions in the Alps. II. Nutrients

Surface sediments from 68 small lakes in the Alps and 9 well-dated sediment core samples that cover a gradient of total phosphorus (TP) concentrations of 6 to 520 μg TP l-1 were studied for diatom, chrysophyte cyst, cladocera, and chironomid assemblages. Inference models for mean circulation log10 TP were developed for diatoms, chironomids, and benthic cladocera using weighted-averaging partial least squares. After screening for outliers, the final transfer functions have coefficients of determination (r2, as assessed by cross-validation, of 0.79 (diatoms), 0.68 (chironomids), and 0.49 (benthic cladocera). Planktonic cladocera and chrysophytes show very weak relationships to TP and no TP inference models were developed for these biota. Diatoms showed the best relationship with TP, whereas the other biota all have large secondary gradients, suggesting that variables other than TP have a strong influence on their composition and abundance. Comparison with other diatom – TP inference models shows that our model has high predictive power and a low root mean squared error of prediction, as assessed by cross-validation.