Comparison of partial least squares regression and principal component regression for pelvic shape prediction.

This paper studied two different regression techniques for pelvic shape prediction, i.e., the partial least square regression (PLSR) and the principal component regression (PCR). Three different predictors such as surface landmarks, morphological parameters, or surface models of neighboring structures were used in a cross-validation study to predict the pelvic shape. Results obtained from applying these two different regression techniques were compared to the population mean model. In almost all the prediction experiments, both regression techniques unanimously generated better results than the population mean model, while the difference on prediction accuracy between these two regression methods is not statistically significant (α=0.01).

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.

Does body mass index in childhood predict restraint eating in early adolescence?

The aim of this study was (1) to examine whether childhood BMI is a significant predictor of restrained eating in preadolescents, (2) to investigate gender differences in restrained and emotional eating, and (3) to determine whether emotional problems, and body esteem were related to eating problems of preadolescents. In this longitudinal study with two measurement points, data from 428 children (50% female) were used. At time 1 (t1) children were on average 5.9 years old. BMI was assessed using objective measures. At time 2 (t2) participants were 12 years old. The adolescents and their parents completed questionnaires assessing restrained and emotional eating, body esteem, emotional problems, and BMI. Multiple regression analysis showed that restrained eating was significantly predicted by t1 BMI, by change in BMI between t1 and t2, and t2 body esteem. Emotional eating was, as expected, not predicted by t1 BMI, but associated with t2 body esteem and t2 emotional problems. Gender was not a significant predictor. The stability of BMI between childhood and preadolescence and its ability to predict restrained eating suggests that it is important to start prevention of overweight, body dissatisfaction and disordered eating at an early age

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.

Aptitude–Treatment interaction effects in psychooncological interventions

Objective: To examine aptitude–treatment interaction (ATI) effects in cancer patients receiving psychoonco- logical interventions (POIs). Method: N=36 cancer patients were treated with POI. Hierarchical linear regression was used to test two interaction effects between patient baseline characteristics (aptitudes) and process analyses of therapy sessions (treatment) on change in mental health during POI. Results: Patients with high emotional distress did best when their therapy reduced arousal, and patients with lower emotional distress benefited most if therapists emphasized arousal induction. The interaction between the coping style of the patient (internalizing vs. externalizing) and the focus of the treatment (emotion vs. behavior) did not predict POI outcomes. Conclusions: The ATI effect of patient's distress and therapist's arousal induction/reduction may help therapists to make differential treatment decisions in POI. Tailoring treatments to cancer patients based on their personal characteristics may enhance the effectiveness of POI.