3D volumetric intensity reconsturction from 2D x-ray images using partial least squares regression

Reconstruction of shape and intensity from 2D x-ray images has drawn more and more attentions. Previously introduced work suffers from the long computing time due to its iterative optimization characteristics and the requirement of generating digitally reconstructed radiographs within each iteration. In this paper, we propose a novel method which uses a patient-specific 3D surface model reconstructed from 2D x-ray images as a surrogate to get a patient-specific volumetric intensity reconstruction via partial least squares regression. No DRR generation is needed. The method was validated on 20 cadaveric proximal femurs by performing a leave-one-out study. Qualitative and quantitative results demonstrated the efficacy of the present method. Compared to the existing work, the present method has the advantage of much shorter computing time and can be applied to both DXA images as well as conventional x-ray images, which may hold the potentials to be applied to clinical routine task such as total hip arthroplasty (THA).

Robust Proximal Femur Segmentation in Conventional X-Ray Images via Random Forest Regression on Multi-resolution Gradient Features

In this paper, we propose a fully automatic, robust approach for segmenting proximal femur in conventional X-ray images. Our method is based on hierarchical landmark detection by random forest regression, where the detection results of 22 global landmarks are used to do the spatial normalization, and the detection results of the 59 local landmarks serve as the image cue for instantiation of a statistical shape model of the proximal femur. To detect landmarks in both levels, we use multi-resolution HoG (Histogram of Oriented Gradients) as features which can achieve better accuracy and robustness. The efficacy of the present method is demonstrated by experiments conducted on 150 clinical x-ray images. It was found that the present method could achieve an average point-to-curve error of 2.0 mm and that the present method was robust to low image contrast, noise and occlusions caused by implants.

Fully Automatic Segmentation of AP Pelvis X-rays via Random Forest Regression and Hierarchical Sparse Shape Composition

Knowledge of landmarks and contours in anteroposterior (AP) pelvis X-rays is invaluable for computer aided diagnosis, hip surgery planning and image-guided interventions. This paper presents a fully automatic and robust approach for landmarking and segmentation of both pelvis and femur in a conventional AP X-ray. Our approach is based on random forest regression and hierarchical sparse shape composition. Experiments conducted on 436 clinical AP pelvis x-rays show that our approach achieves an average point-to-curve error around 1.3 mm for femur and 2.2 mm for pelvis, both with success rates around 98%. Compared to existing methods, our approach exhibits better performance in both the robustness and the accuracy.

Low rank subspace clustering (LRSC)

We consider the problem of fitting a union of subspaces to a collection of data points drawn from one or more subspaces and corrupted by noise and/or gross errors. We pose this problem as a non-convex optimization problem, where the goal is to decompose the corrupted data matrix as the sum of a clean and self-expressive dictionary plus a matrix of noise and/or gross errors. By self-expressive we mean a dictionary whose atoms can be expressed as linear combinations of themselves with low-rank coefficients. In the case of noisy data, our key contribution is to show that this non-convex matrix decomposition problem can be solved in closed form from the SVD of the noisy data matrix. The solution involves a novel polynomial thresholding operator on the singular values of the data matrix, which requires minimal shrinkage. For one subspace, a particular case of our framework leads to classical PCA, which requires no shrinkage. For multiple subspaces, the low-rank coefficients obtained by our framework can be used to construct a data affinity matrix from which the clustering of the data according to the subspaces can be obtained by spectral clustering. In the case of data corrupted by gross errors, we solve the problem using an alternating minimization approach, which combines our polynomial thresholding operator with the more traditional shrinkage-thresholding operator. Experiments on motion segmentation and face clustering show that our framework performs on par with state-of-the-art techniques at a reduced computational cost.

Predicting childhood sexual or physical abuse: a logistic regression geo-mapping approach to prevention.

This study investigates the degree to which gender, ethnicity, relationship to perpetrator, and geomapped socio-economic factors significantly predict the incidence of childhood sexual abuse, physical abuse and non- abuse. These variables are then linked to geographic identifiers using geographic information system (GIS) technology to develop a geo-mapping framework for child sexual and physical abuse prevention.

Progressive regression of left ventricular hypertrophy two years after bariatric surgery.

BACKGROUND: Obesity is a systemic disorder associated with an increase in left ventricular mass and premature death and disability from cardiovascular disease. Although bariatric surgery reverses many of the hormonal and hemodynamic derangements, the long-term collective effects on body composition and left ventricular mass have not been considered before. We hypothesized that the decrease in fat mass and lean mass after weight loss surgery is associated with a decrease in left ventricular mass. METHODS: Fifteen severely obese women (mean body mass index [BMI]: 46.7+/-1.7 kg/m(2)) with medically controlled hypertension underwent bariatric surgery. Left ventricular mass and plasma markers of systemic metabolism, together with body mass index (BMI), waist and hip circumferences, body composition (fat mass and lean mass), and resting energy expenditure were measured at 0, 3, 9, 12, and 24 months. RESULTS: Left ventricular mass continued to decrease linearly over the entire period of observation, while rates of weight loss, loss of lean mass, loss of fat mass, and resting energy expenditure all plateaued at 9 [corrected] months (P <.001 for all). Parameters of systemic metabolism normalized by 9 months, and showed no further change at 24 months after surgery. CONCLUSIONS: Even though parameters of obesity, including BMI and body composition, plateau, the benefits of bariatric surgery on systemic metabolism and left ventricular mass are sustained. We propose that the progressive decrease of left ventricular mass after weight loss surgery is regulated by neurohumoral factors, and may contribute to improved long-term survival.

Plotting regression coefficients and other estimates in Stata

Graphical presentation of regression results has become increasingly popular in the scientific literature, as graphs are much easier to read than tables in many cases. In Stata such plots can be produced by the -marginsplot- command. However, while -marginsplot- is very versatile and flexible, it has two major limitations: it can only process results left behind by -margins- and it can only handle one set of results at the time. In this article I introduce a new command called -coefplot- that overcomes these limitations. It plots results from any estimation command and combines results from several models into a single graph. The default behavior of -coefplot- is to plot markers for coefficients and horizontal spikes for confidence intervals. However, -coefplot- can also produce various other types of graphs. The capabilities of -coefplot- are illustrated in this article using a series of examples.

COEFPLOT: Stata module to plot regression coefficients and other results

coefplot plots results from estimation commands or Stata matrices. Results from multiple models or matrices can be combined in a single graph. The default behavior of coefplot is to draw markers for coefficients and horizontal spikes for confidence intervals. However, coefplot can also produce various other types of graphs.

Real-life results of balloon kyphoplasty for vertebral compression fractures from the SWISSspine registry

BACKGROUND CONTEXT The Swiss Federal Office of Public Health mandated a nationwide health technology assessment-registry for balloon kyphoplasty (BKP) for decision making on reimbursement of these interventions. The early results of the registry led to a permanent coverage of BKP by basic health insurance. The documentation was continued for further evidence generation. PURPOSE This analysis reports on the 1 year results of patients after BKP treatment. STUDY DESIGN Prospective multicenter observational case series. PATIENT SAMPLE The data on 625 cases with 819 treated vertebrae were documented from March 2005 to May 2012. OUTCOME MEASURES Surgeon-administered outcome instruments were primary intervention form for BKP and the follow-up form; patient self-reported measures were EuroQol-5D questionnaire, North American Spine Society outcome instrument /Core Outcome Measures Index (including visual analog scale), and a comorbidity questionnaire. Outcome measures were back pain, medication, quality of life (QoL), cement extrusions, and new fractures within the first postoperative year. METHODS Data were recorded preoperatively and at 3 to 6-month and 1-year follow-ups. Wilcoxon signed-rank test was used for comparison of pre- with postoperative measurements. Multivariate logistic regression was used to identify factors with a significant influence on the outcome. RESULTS Seventy percent of patients were women with mean age of 71 years (range, 18-91 years); mean age of men was 65 years (range, 15-93 years). Significant and clinically relevant reduction of back pain, improvement of QoL, and reduction of pain killer consumption was seen within the first postoperative year. Preoperative back pain decreased from 69.3 to 29.0 at 3 to 6-month and remained unchanged at 1-year follow-ups. Consequently, QoL improved from 0.23 to 0.71 and 0.75 at the same follow-up intervals. The overall vertebra-based cement extrusion rates with and without extrusions into intervertebral discs were 22.1% and 15.3%, respectively. Symptomatic cement extrusions with radiculopathy were five (0.8%). A new vertebral fracture within a year from the BKP surgery was observed in 18.4% of the patients. CONCLUSIONS The results of the largest observational study for BKP so far are consistent with published randomized trials and systematic reviews. In this routine health care setting, BKP is safe and effective in reducing pain, improving QoL, and lowering pain_killer consumption and has an acceptable rate of cement extrusions. Postoperative outcome results show clear and significant clinical improvement at early follow-up that remain stable during the first postoperative year.

Regression of the inflammatory microenvironment of the peritoneal cavity in women with endometriosis by GnRHa treatment.

OBJECTIVE To investigate the effect of gonadotropin-releasing hormone analogues (GnRHa) on the peritoneal fluid microenvironment in women with endometriosis. STUDY DESIGN Peritoneal fluid was collected from 85 women with severe endometriosis (rAFS stage III and IV) during laparoscopic surgery during the proliferative phase. Prior to surgery clinical data were collected. The concentrations of specific markers for endometriosis in the peritoneal fluid were determined using an ELISA and a comparison between peritoneal fluid markers in women using GnRHa and no hormonal treatment was performed using a non-parametric Mann-Whitney U test. RESULTS The study included peritoneal fluid from 39 patients who had been administered GnRHa (Zoladex(®)) in the three months prior to surgery and 46 from women with no hormonal treatment in this period. Concentrations of IL-8, PAPP-A, glycodelin-A and midkine were significantly reduced in the GnRHa treatment group compared to women receiving no hormonal treatment. RANTES, MCP-1, ENA-78, TNF-α, OPG, IP-10 and defensin showed no significant change between the two groups. CONCLUSIONS GnRHa mediate a significant regression in the inflammatory nature of the peritoneal microenvironment in women with endometriosis.

Detecting determinism with improved sensitivity in time series: Rank-based nonlinear predictability score

The rank-based nonlinear predictability score was recently introduced as a test for determinism in point processes. We here adapt this measure to time series sampled from time-continuous flows. We use noisy Lorenz signals to compare this approach against a classical amplitude-based nonlinear prediction error. Both measures show an almost identical robustness against Gaussian white noise. In contrast, when the amplitude distribution of the noise has a narrower central peak and heavier tails than the normal distribution, the rank-based nonlinear predictability score outperforms the amplitude-based nonlinear prediction error. For this type of noise, the nonlinear predictability score has a higher sensitivity for deterministic structure in noisy signals. It also yields a higher statistical power in a surrogate test of the null hypothesis of linear stochastic correlated signals. We show the high relevance of this improved performance in an application to electroencephalographic (EEG) recordings from epilepsy patients. Here the nonlinear predictability score again appears of higher sensitivity to nonrandomness. Importantly, it yields an improved contrast between signals recorded from brain areas where the first ictal EEG signal changes were detected (focal EEG signals) versus signals recorded from brain areas that were not involved at seizure onset (nonfocal EEG signals).

The IFNL3/4 ΔG variant increases susceptibility to cytomegalovirus retinitis among HIV-infected patients.

BACKGROUND Cytomegalovirus (CMV) retinitis is a major cause of visual impairment and blindness among patients with uncontrolled HIV infections. Whereas polymorphisms in interferon-lambda 3 (IFNL3, previously named IL28B) strongly influence the clinical course of hepatitis C, few studies examined the role of such polymorphisms in infections due to viruses other than hepatitis C virus. OBJECTIVES To analyze the association of newly identified IFNL3/4 variant rs368234815 with susceptibility to CMV-associated retinitis in a cohort of HIV-infected patients. DESIGN AND METHODS This retrospective longitudinal study included 4884 white patients from the Swiss HIV Cohort Study, among whom 1134 were at risk to develop CMV retinitis (CD4 nadir <100 /μl and positive CMV serology). The association of CMV-associated retinitis with rs368234815 was assessed by cumulative incidence curves and multivariate Cox regression models, using the estimated date of HIV infection as a starting point, with censoring at death and/or lost follow-up. RESULTS A total of 40 individuals among 1134 patients at risk developed CMV retinitis. The minor allele of rs368234815 was associated with a higher risk of CMV retinitis (log-rank test P = 0.007, recessive mode of inheritance). The association was still significant in a multivariate Cox regression model (hazard ratio 2.31, 95% confidence interval 1.09-4.92, P = 0.03), after adjustment for CD4 nadir and slope, HAART and HIV-risk groups. CONCLUSION We reported for the first time an association between an IFNL3/4 polymorphism and susceptibility to AIDS-related CMV retinitis. IFNL3/4 may influence immunity against viruses other than HCV.