scholarly journals An automated approach for three-dimensional quantification of fibrillar structures in optically cleared soft biological tissues

2013 ◽  
Vol 10 (80) ◽  
pp. 20120760 ◽  
Author(s):  
Andreas J. Schriefl ◽  
Heimo Wolinski ◽  
Peter Regitnig ◽  
Sepp D. Kohlwein ◽  
Gerhard A. Holzapfel
Keyword(s):  
Second Harmonic ◽  
Structural Parameters ◽  
Three Dimensional ◽  
Collagen Fibre ◽  
Likelihood Estimation ◽  
Biological Tissues ◽  
Distribution Fitting ◽  
Soft Biological Tissues ◽  
Tissue Samples ◽  
Novel Approach

We present a novel approach allowing for a simple, fast and automated morphological analysis of three-dimensional image stacks ( z -stacks) featuring fibrillar structures from optically cleared soft biological tissues. Five non-atherosclerotic tissue samples from human abdominal aortas were used to outline the multi-purpose methodology, applicable to various tissue types. It yields a three-dimensional orientational distribution of relative amplitudes, representing the original collagen fibre morphology, identifies regions of isotropy where no preferred fibre orientations are observed and determines structural parameters throughout anisotropic regions for the analysis and numerical modelling of biomechanical quantities such as stress and strain. Our method combines optical tissue clearing with second-harmonic generation imaging, Fourier-based image analysis and maximum-likelihood estimation for distribution fitting. With a new sample preparation method for arteries, we present, for the first time to our knowledge, a continuous three-dimensional distribution of collagen fibres throughout the entire thickness of the aortic wall, revealing novel structural and organizational insights into the three arterial layers.

scholarly journals Predictive constitutive modelling of arteries by deep learning

2021 ◽  
Vol 18 (182) ◽  
pp. 20210411
Author(s):  
Gerhard A. Holzapfel ◽  
Kevin Linka ◽  
Selda Sherifova ◽  
Christian J. Cyron
Keyword(s):  
Mechanical Properties ◽  
Deep Learning ◽  
Second Harmonic ◽  
Biological Tissues ◽  
Constitutive Modelling ◽  
Coefficient Of Determination ◽  
Soft Biological Tissues ◽  
Tissue Samples ◽  
Hybrid Modelling ◽  
Modelling Framework

The constitutive modelling of soft biological tissues has rapidly gained attention over the last 20 years. Current constitutive models can describe the mechanical properties of arterial tissue. Predicting these properties from microstructural information, however, remains an elusive goal. To address this challenge, we are introducing a novel hybrid modelling framework that combines advanced theoretical concepts with deep learning. It uses data from mechanical tests, histological analysis and images from second-harmonic generation. In this first proof of concept study, our hybrid modelling framework is trained with data from 27 tissue samples only. Even such a small amount of data is sufficient to be able to predict the stress–stretch curves of tissue samples with a median coefficient of determination of R 2 = 0.97 from microstructural information, as long as one limits the scope to tissue samples whose mechanical properties remain in the range commonly encountered. This finding suggests that deep learning could have a transformative impact on the way we model the constitutive properties of soft biological tissues.

scholarly journals On fibre dispersion modelling of soft biological tissues: a review

2019 ◽  
Vol 475 (2224) ◽  
pp. 20180736 ◽  
Author(s):  
Gerhard A. Holzapfel ◽  
Ray W. Ogden ◽  
Selda Sherifova
Keyword(s):  
Heart Valves ◽  
Mechanical Response ◽  
Structural Information ◽  
Second Harmonic ◽  
Biological Tissues ◽  
Mechanical Tests ◽  
Soft Biological Tissues ◽  
Tensor Models ◽  
Continuum Modelling ◽  
Stress And Deformation

Collagen fibres within fibrous soft biological tissues such as artery walls, cartilage, myocardiums, corneas and heart valves are responsible for their anisotropic mechanical behaviour. It has recently been recognized that the dispersed orientation of these fibres has a significant effect on the mechanical response of the tissues. Modelling of the dispersed structure is important for the prediction of the stress and deformation characteristics in (patho)physiological tissues under various loading conditions. This paper provides a timely and critical review of the continuum modelling of fibre dispersion, specifically, the angular integration and the generalized structure tensor models. The models are used in representative numerical examples to fit sets of experimental data that have been obtained from mechanical tests and fibre structural information from second-harmonic imaging. In particular, patches of healthy and diseased aortic tissues are investigated, and it is shown that the predictions of the models fit very well with the data. It is straightforward to use the models described herein within a finite-element framework, which will enable more realistic (and clinically relevant) boundary-value problems to be solved. This also provides a basis for further developments of material models and points to the need for additional mechanical and microstructural data that can inform further advances in the material modelling.

High-Resolution Three-Dimensional Microscopy System

2000 ◽  
Vol 6 (S2) ◽  
pp. 1012-1013
Author(s):  
Russell Kerschmann
Keyword(s):  
Three Dimensional ◽  
Human Tumor ◽  
Biological Tissues ◽  
Volumetric Analysis ◽  
Cellular Level ◽  
Optical Sectioning ◽  
Tissue Samples ◽  
Main Method ◽  
New Methods ◽  
Powerful Research Tool

The demand for new methods of three-dimensional imaging of biological systems grown significantly over the past decades. Systems for volumetric analysis of macroscopic structures have been addressed through the introduction of modem CT/MRI systems; and on the cellular level, confocal microscopy has evolved into a powerful research tool for the examination of both biological tissues and manufactured goods. However, there persists a need for the visualization and analysis of types of material in the cubic millimeter size range, a class of materials which has not been adequately addressed by either radiological or optical sectioning techniques. These materials include research and clinical biological tissue samples, as well as many types of manufactured materials such as textiles and paper.The main method currently in use for the examination of such materials is standard histopathology. Whether one is concerned with the diagnosis of a human tumor or the arrangement of cells in the leaf of a plant,

The “Gene Cube”: A Novel Approach to Three-dimensional Clustering of Gene Expression Data

2019 ◽  
Vol 14 (8) ◽  
pp. 721-727 ◽  
Author(s):  
George I. Lambrou ◽  
Maria Sdraka ◽  
Dimitrios Koutsouris
Keyword(s):  
Gene Expression ◽  
Clustering Algorithms ◽  
Expression Patterns ◽  
Three Dimensional ◽  
Dimensional Structure ◽  
Tissue Samples ◽  
Novel Approach ◽  
Local Background ◽  
3D Matrix ◽  
Public Datasets

Background: A very popular technique for isolating significant genes from cancerous tissues is the application of various clustering algorithms on data obtained by DNA microarray experiments. Aim: The objective of the present work is to take into consideration the chromosomal identity of every gene before the clustering, by creating a three-dimensional structure of the form Chromosomes×Genes×Samples. Further on, the k-Means algorithm and a triclustering technique called δ- TRIMAX, are applied independently on the structure. Materials and Methods: The present algorithm was developed using the Python programming language (v. 3.5.1). For this work, we used two distinct public datasets containing healthy control samples and tissue samples from bladder cancer patients. Background correction was performed by subtracting the median global background from the median local Background from the signal intensity. The quantile normalization method has been applied for sample normalization. Three known algorithms have been applied for testing the “gene cube”, a classical k-means, a transformed 3D k-means and the δ-TRIMAX. Results: Our proposed data structure consists of a 3D matrix of the form Chromosomes×Genes×Samples. Clustering analysis of that structure manifested very good results as we were able to identify gene expression patterns among samples, genes and chromosomes. Discussion: to the best of our knowledge, this is the first time that such a structure is reported and it consists of a useful tool towards gene classification from high-throughput gene expression experiments. Conclusion: Such approaches could prove useful towards the understanding of disease mechanics and tumors in particular.

scholarly journals An exponential constitutive model excluding fibres under compression: Application to extension–inflation of a residually stressed carotid artery

2017 ◽  
Vol 23 (8) ◽  
pp. 1206-1224 ◽  
Author(s):  
Kewei Li ◽  
Ray W Ogden ◽  
Gerhard A Holzapfel
Keyword(s):  
Finite Element ◽  
Carotid Artery ◽  
Constitutive Model ◽  
Dispersion Model ◽  
Three Dimensional ◽  
Uniaxial Extension ◽  
Collagen Fibre ◽  
Strain Energy Function ◽  
Biological Tissues ◽  
Finite Element Program

Detailed information on the three-dimensional dispersion of collagen fibres within layers of healthy and diseased soft biological tissues has been reported recently. Previously we have proposed a constitutive model for soft fibrous solids based on the angular integration approach which allows the exclusion of any compressed collagen fibre within the dispersion. In addition, a computational implementation of that model in a general purpose finite element program has been investigated and verified with the standard fibre-reinforcing model for fibre contributions. In this study, we develop the proposed fibre dispersion model further using an exponential form of the strain-energy function for the fibre contributions. The finite element implementation of this model with a rotationally symmetrical dispersion of fibres is also presented. This includes explicit expressions for the stress and elasticity tensors. The performance and implementation of the new model are demonstrated by means of a uniaxial extension test, a simple shear test, and an extension–inflation simulation of a residually stressed carotid artery segment. In each example we have obtained good agreement between the finite element solution and the analytical or experimental results.

scholarly journals Three-Dimensional High-Resolution Second-Harmonic Generation Imaging of Endogenous Structural Proteins in Biological Tissues

2002 ◽  
Vol 82 (1) ◽  
pp. 493-508 ◽  
Author(s):  
Paul J. Campagnola ◽  
Andrew C. Millard ◽  
Mark Terasaki ◽  
Pamela E. Hoppe ◽  
Christian J. Malone ◽  
...  
Keyword(s):  
High Resolution ◽  
Second Harmonic Generation ◽  
Harmonic Generation ◽  
Second Harmonic ◽  
Three Dimensional ◽  
Biological Tissues ◽  
Structural Proteins ◽  
Second Harmonic Generation Imaging

scholarly journals Quantitative assessment of collagen fibre orientations from two-dimensional images of soft biological tissues

2012 ◽  
Vol 9 (76) ◽  
pp. 3081-3093 ◽  
Author(s):  
Andreas J. Schriefl ◽  
Andreas J. Reinisch ◽  
Sethuraman Sankaran ◽  
David M. Pierce ◽  
Gerhard A. Holzapfel
Keyword(s):  
Computational Modelling ◽  
Collagen Fibre ◽  
Biological Tissues ◽  
Fibre Reinforcement ◽  
Two Dimensional ◽  
Soft Biological Tissues ◽  
Length Scales ◽  
Automated Method ◽  
Principal Directions ◽  
Two Dimensional Images

In this work, we outline an automated method for the extraction and quantification of material parameters characterizing collagen fibre orientations from two-dimensional images. Morphological collagen data among different length scales were obtained by combining the established methods of Fourier power spectrum analysis, wedge filtering and progressive regions of interest splitting. Our proposed method yields data from which we can determine parameters for computational modelling of soft biological tissues using fibre-reinforced constitutive models and gauge the length scales most appropriate for obtaining a physically meaningful measure of fibre orientations, which is representative of the true tissue morphology of the two-dimensional image. Specifically, we focus on three parameters quantifying different aspects of the collagen morphology: first, using maximum-likelihood estimation, we extract location parameters that accurately determine the angle of the principal directions of the fibre reinforcement (i.e. the preferred fibre directions); second, using a dispersion model, we obtain dispersion parameters quantifying the collagen fibre dispersion about these principal directions; third, we calculate the weighted error entropy as a measure of changes in the entire fibre distributions at different length scales, as opposed to their average behaviour. With fully automated imaging techniques (such as multiphoton microscopy) becoming increasingly popular (which often yield large numbers of images to analyse), our method provides an ideal tool for quickly extracting mechanically relevant tissue parameters which have implications for computational modelling (e.g. on the mesh density) and can also be used for the inhomogeneous modelling of tissues.

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