Determination of constitutive properties of cells is important for quantitative description of cellular mechanics.
Existing approaches to mechanical cell manipulation are based on experimental techniques that do not allow
unsupervised analysis of large number of cells and/or probing of intracellular structures that are not directly
exposed to external loads. Alternatively, mechanical behavior of cellular matter can be studied in time-series
of microscopic images. In this work, we present an image- and model-based framework for determination of
constitutive properties of living cells. Our experimental studies demonstrate application of this approach for
quantitative analysis of cellular mechanics on the basis of image data assessed by different experimental techniques,
including microplate stretching, optical stretching and contactless cellular deformation induction using
cytoskeleton-disrupting drugs.
Unsupervised analysis of time-series of live-cell images is one of the important tools of quantitative biology.
Due to permanent cell motility or displacements of subcellular structures, microscopic images exhibit intrinsic
non-uniform motion. In this article, we present a novel approach for detection of non-uniform multi-body motion
which is based on combination of the Fourier-phase correlation with iterative probing target and background
image regions similar to the strategy known from saccadic eye movements. We derive theoretical expressions
that yield plausible explanation why this strategy turns out to be advantageous for tracking particular image
pattern. Our experiments with synthetic and live-cell images demonstrate that the proposed approach is capable
of accurately detecting non-uniform motion in synthetic and live-cell images.
Linear elastic model widely applied for simulation of soft tissue deformations in biomedical imaging applications is
basically limited to the range of small deformations and rotations. Thus, computation of large deformations and
rotations using linear elastic approximation and its derivatives is associated with substantial error. More realistic
modeling of mechanical behavior of soft tissue requires handling of different types of nonlinearities. This paper
presents a framework for more accurate modeling of deformable structures based on the St. Venant-Kirchhoff
law with the nonlinear Green-Lagrange strain tensor and variable material constants, which considers both
material and geometric nonlinearities. We derive the governing partial differential equation of nonlinear elasticity,
which represents consistent extension of the Lame-Navier PDE of linear elasticity, and describe two alternative
numerical schemes for solving this nonlinear PDE via the Newton's and fixed point method, respectively. The
results of our comparative studies demonstrate the advantages of nonlinear elastic model for accurate computing
of large deformations and rotations in comparison to the linear elastic approximation.
High-throughput live-cell imaging is one of the important tools for the investigation of cellular structure and
functions in modern experimental biology. Automatic processing of time series of microscopic images is hampered
by a number of technical and natural factors such as permanent movements of cells in the optical field, alteration
of optical cell appearance and high level of noise. Detection and compensation of global motion of groups of cells
or relocation of a single cell within a dynamical multi-cell environment is the first indispensable step in the image
analysis chain. This article presents an approach for detection of global image motion and single cell tracking in
time series of confocal laser scanning microscopy images using an extended Fourier-phase correlation technique,
which allows for analysis of non-uniform multi-body motion in partially-similar images. Our experimental results
have shown that the developed approach is capable to perform cell tracking and registration in dynamical and
noisy scenes, and provides a robust tool for fully-automatic registration of time-series of microscopic images.
Topological analysis of cells and subcellular structures on the basis of image data is one of the major trends in modern quantitative biology. However, due to the dynamic nature of cell biology, the optical appearance of different cells or even time series of the same cell is undergoing substantial variations in shape and texture which makes the analysis of image data a non-trivial task. In the absence of canonical invariances, a natural approach to the normalization of cell images consists in dimension reduction of the 3D problem by means of spherical mapping which enables the analysis of targeted regions in terms of radial distances. In this work, we present a finite element template-based approach for physically-base spherical mapping which has been applied for topological analysis of confocal laser scanning microscopy images of cell nuclei.
This work presents a novel approach for the physically-based optimization of individual implants in cranio-maxillofacial surgery. The proposed method is based on solving an inverse boundary value problem of the cranio-maxillofacial surgery planning, i.e. finding an optimal implant shape for a desired correction of soft tissues. The paper describes the methodology for the generation of individual geometrical models of human head, the reverse finite element approach for solving biomechanical boundary value problems and two clinical studies dealing with the computer aided design of individual craniofacial implants.
This work presents an approach for contour-based shape description, which addresses a large number of cognitive
problems related to classification and recognition of 2D single closed contours. Exploiting basic observations
about the shape invariant properties, we construct a normalization procedure that establishes the ground for
generalized similarity matching between arbitrarily translated, rotated and scaled 2D shapes. A novel descriptor
for global and detailed shape analysis is proposed. Cognitive experiments show that the proposed method suits
for the recognition of typical silhouettes of conservative anatomical structures, e.g. outlines of vertebra or inner
organs. Common issues for optimal data pre-processing and shape representation are discussed.
KEYWORDS: Tissues, 3D modeling, Data modeling, Surgery, Tomography, Finite element methods, Chemical elements, Solid modeling, Image segmentation, Visual process modeling
In addition to the static soft tissue prediction, the estimation of individual facial emotion expressions is an important criterion for the evaluation of the carniofacial surgery planning. In this paper, we present an approach for the estimation of individual facial emotion expressions on the basis of geometrical models of human anatomy derived from tomographic data and the finite element modeling of facial tissue biomechanics.
In craniofacial surgery simulations, the realistic prediction of a patient's postoperative appearance is of utmost importance. Due to the complexity of biomechanical behavior of soft tissue simplified models such as widespread linear elastic models are usually applied. However, large deformations often occurring in craniofacial surgery can't be accurately modeled via linear elastic approach, since it is generally limited by the assumption of small deformations. In this paper, we present an adaptive nonlinear elastic approach based on the finite element discretization of 3D patient's model, which is free of linearization limitations and yields more realistic results in simulations of large facial tissue deformation.
Besides the static soft tissue prediction, the estimation of basic facial emotion expressions is another important criterion for the evaluation of craniofacial surgery planning. For a realistic simulation of facial mimics, an adequate biomechanical model of soft tissue including the mimic musculature is needed. In this work, we present an approach for the modeling of arbitrarily shaped muscles and the estimation of basic individual facial mimics, which is based on the geometrical model derived from the individual tomographic data and the general finite element modeling of soft tissue biomechanics.
Physically based soft tissue modeling is a state of the art in computer assisted surgery (CAS). But even such a sophisticated approach has its limits. The biomechanic behavior of soft tissue is highly complex, so that simplified models have to be applied. Under assumption of small deformations, usually applied in soft tissue modeling, soft tissue can be approximately described as a linear elastic continuum. Since there exist efficient techniques for solving linear partial differential equations, the linear elastic model allows comparatively fast calculation of soft tissue deformation and consequently the prediction of a patient's postoperative appearance. However, for the calculation of large deformations, which are not unusual in craniofacial surgery, this approach can implicate substantial error depending on the intensity of the deformation. The monitoring of the linearization error could help to estimate the scope of validity of calculations upon user defined precision. In order to quantify this error one even do not need to know the correct solution, since the linear theory implies the appropriate instruments for error detection in itself.
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