Optic nerve head three-dimensional shape analysis

We present a method for optic nerve head (ONH) 3-D shape analysis from retinal optical coherence tomography (OCT). The possibility to noninvasively acquire in vivo high-resolution 3-D volumes of the ONH using spectral domain OCT drives the need to develop tools that quantify the shape of this structure and extract information for clinical applications. The presented method automatically generates a 3-D ONH model and then allows the computation of several 3-D parameters describing the ONH. The method starts with a high-resolution OCT volume scan as input. From this scan, the model-defining inner limiting membrane (ILM) as inner surface and the retinal pigment epithelium as outer surface are segmented, and the Bruch's membrane opening (BMO) as the model origin is detected. Based on the generated ONH model by triangulated 3-D surface reconstruction, different parameters (areas, volumes, annular surface ring, minimum distances) of different ONH regions can then be computed. Additionally, the bending energy (roughness) in the BMO region on the ILM surface and 3-D BMO-MRW surface area are computed. We show that our method is reliable and robust across a large variety of ONH topologies (specific to this structure) and present a first clinical application

Parameter identification problems in the modelling of cell motility

We present a novel parameter identification algorithm for the estimation of parameters in models of cell motility using imaging data of migrating cells. Two alternative formulations of the objective functional that measures the difference between the computed and observed data are proposed and the parameter identification problem is formulated as a minimisation problem of nonlinear least squares type. A Levenberg–Marquardt based optimisation method is applied to the solution of the minimisation problem and the details of the implementation are discussed. A number of numerical experiments are presented which illustrate the robustness of the algorithm to parameter identification in the presence of large deformations and noisy data and parameter identification in three dimensional models of cell motility. An application to experimental data is also presented in which we seek to identify parameters in a model for the monopolar growth of fission yeast cells using experimental imaging data. Our numerical tests allow us to compare the method with the two different formulations of the objective functional and we conclude that the results with both objective functionals seem to agree

A variational formulation of anisotropic geometric evolution equations in higher dimensions

Accepted versio

Modular deep neural networks for automatic quality control of retinal optical coherence tomography scans

Retinal optical coherence tomography (OCT) with intraretinal layer segmentation is increasingly used not only in ophthalmology but also for neurological diseases such as multiple sclerosis (MS). Signal quality influences segmentation results, and high-quality OCT images are needed for accurate segmentation and quantification of subtle intraretinal layer changes. Among others, OCT image quality depends on the ability to focus, patient compliance and operator skills. Current criteria for OCT quality define acceptable image quality, but depend on manual rating by experienced graders and are time consuming and subjective. In this paper, we propose and validate a standardized, grader-independent, real-time feedback system for automatic quality assessment of retinal OCT images. We defined image quality criteria for scan centering, signal quality and image completeness based on published quality criteria and typical artifacts identified by experienced graders when inspecting OCT images. We then trained modular neural networks on OCT data with manual quality grading to analyze image quality features. Quality analysis by a combination of these trained networks generates a comprehensive quality report containing quantitative results. We validated the approach against quality assessment according to the OSCAR-IB criteria by an experienced grader. Here, 100 OCT files with volume, circular and radial scans, centered on optic nerve head and macula, were analyzed and classified. A specificity of 0.96, a sensitivity of 0.97 and an accuracy of 0.97 as well as a Matthews correlation coefficient of 0.93 indicate a high rate of correct classification. Our method shows promising results in comparison to manual OCT grading and may be useful for real-time image quality analysis or analysis of large data sets, supporting standardized application of image quality criteria

From Nanometer Aggregates to Micrometer Crystals: Insight into the Coarsening Mechanism of Calcite

Grain size increases when crystals respond to dynamic equilibrium in a saturated solution. The pathway to coarsening is generally thought to be driven by Ostwald ripening, that is, simultaneous dissolution and reprecipitation, but models to describe Ostwald ripening neglect solid–solid interactions and crystal shapes. Grain coarsening of calcite, CaCO₃, is relevant for biomineralization and commercial products and is an important process in diagenesis of sediments to rock during geological time. We investigated coarsening of pure, synthetic calcite powder of sub-micrometer diameter crystals and aged it in saturated solutions at 23, 100, and 200 °C for up to 261 days. Scanning electron microscopy (SEM) and Brunauer–Emmett–Teller (BET) surface area analysis showed rapid coarsening at 100 and 200 °C. Evidence of particle growth at 23 °C was not visible by SEM, but high resolution X-ray diffraction (XRD) data demonstrated steady growth of nanometer crystallites. The results can be described by theory where grains coarsen preferentially by aggregation at early times and high temperatures and by Ostwald ripening at later stages. Crystal form and dimension are influenced by the transition from one growth mechanism to the other. This has been poorly described by mean field coarsening models and offers predictive power to grain coarsening models

Ethnic identity and scholastic effort: a multifaceted approach

When one analyses the influence of social identity on scholastic effort, ethnic identity largely contributes to determine it. In this paper, ethnic identity is meant as the attachment to one’s cultural heritage and the adaptation to host societies; this allows considering how conflicting demands and social pressure from parents, peers, ethnic community and host societies influence children’s effort. Attention is also paid to the locus of control; thereby, the effects of the interaction between the social context—ethnic identity—and personal traits such as the locus of control are considered. The analysis is developed through a theoretical model whose results partly show that children’s effort may be influenced positively by parents with strong attitudes towards adaptation and negatively by their peers in school who belong to marginalized groups vulnerable to discrimination and convinced that school does not improve one’s socio-economic status. Nevertheless, the drawbacks of the social context can be counterbalanced by a strong locus of control