Bayesian registration of models using finite element eigenmodes.

This paper is concerned with registering three-dimensional wire-frame organ models. This involves finding correspondences between points on the models of two different examples of the same organ. Such registration is widely used in the processing of medical data; for example in segmentation, or to superimpose functional information on a more detailed structural map. The algorithm described in this paper is based on matching the modes of deformation of organ shapes. Modes with lower spatial frequency characterise large scale organ features whereas small scale variations determine the high frequency modes. First, the organ sizes are normalised using a generalised version of the centroid size metric. The axes of the fundamental frequency modes are then aligned to provide initial rigid-body registration. The registration is refined by matching increasingly high frequency modes using the 'Highest confidence first' algorithm. The matches are evaluated using a Bayesian combination of local prior and likelihood functions. The prior is derived from the Gompertz metric of biological growth and ensures that physically impossible matches are not accepted. The likelihood function is a measure of the similarity between local modal deformation components. The registration algorithm has been applied by the authors in the analysis of three dimensional ultrasound data. Results are presented showing the registration of two liver models derived from 3D ultrasound

Interactive segmentation of 3d ultrasound using deformable solid models and active contours

This paper presents a prototype segmentation system for three-dimensional ultrasound data. 3D ultrasound is cheap and noninvasive but the data has a low signal-to-noise ratio and contains artifacts. To overcome these difficulties we have developed a system which uses a prior model, initialised by a clinician, to provide the starting point for a data-driven segmentation algorithm based on active contours. Results are presented showing how the technique can facilitate the segmentation of a gall-bladder

Configurations of mother-child and father-child attachment as predictors of internalizing and externalizing behavioral problems: An individual participant data (IPD) meta-analysis

An unsettled question in attachment theory and research is the extent to which children's attachment patterns with mothers and fathers jointly predict developmental outcomes. In this study, we used individual participant data (IPD) meta-analysis to assess whether early attachment networks with mothers and fathers are associated with children's internalizing and externalizing behavioral problems. Following a pre-registered protocol, data from 9 studies and 1,097 children (mean age: 28.67 months) with attachment classifications to both mothers and fathers were included in analyses. We used a linear mixed effects analysis to assess differences in children's internalizing and externalizing behavioral problems as assessed via the average of both maternal and paternal reports based on whether children had two, one, or no insecure (or disorganized) attachments. Results indicated that children with an insecure attachment relationship with one or both parents were at higher risk for elevated internalizing behavioral problems compared with children who were securely attached to both parents. Children whose attachment relationships with both parents were classified as disorganized had more externalizing behavioral problems compared to children with either one or no disorganized attachment relationship with their parents. Across attachment classification networks and behavioral problems, findings suggest (a) an increased vulnerability to behavioral problems when children have insecure or disorganized attachment to both parents, and (b) that mother-child and father-child attachment relationships may not differ in the roles they play in children's development of internalizing and externalizing behavioral problems