Custom image analysis
Image analysis based on commercial and open source software as well as custom programs and scripts are used in many applications of our research. For example, a fast optical-flow software to track features of deforming specimen surfaces and thus to obtain image-based estimates of the displacement field.
This and other custom codes allow us determine local states of strain in e.g. tensile tests and inflation experiments on the one hand [1], but also from in-situ microscopy techniques [2,3] and medical imaging such as MRI or ultrasound [4,5].
Moreover, image analysis together with microscopy enables us to set-up computational models that reflect the microstructure of biological tissues at different length scales. Based on analyzing scanning electron microscopy and micro computed tomography data, for example, we have generated and validated models of electrospun fiber networks [6,7] The analysis and segmentation of brightfield histological sections allowed defining detailed models of skeletal muscle tissue at the length scale of a few hundred micrometers [8-10].
[1] Hopf R., Bernardi L., Menze J., Zündel M., Mazza E., Ehret A.E. (2016) Experimental and theoretical analyses of the age-dependent large-strain behavior of Sylgard 184 (10:1) silicone elastomer. J. Mech Behav. Biomed. Mater. 60, 425-437. DOI:10.1016/j.jmbbm.2016.02.022.
[2] Ehret A.E., Bircher K., Stracuzzi A., Marina V., Zundel M., Mazza E. (2017) Inverse poroelasticity as a fundamental mechanism in biomechanics and mechanobiology. Nat. Commun. 8, 1002. DOI: 10.1038/s41467-017-00801-3
[3] Mauri, A., Ehret, A. E., Perrini, M., Maake, C., Ochsenbein-Kölble, N., Ehrbar, M., Oyen M.L., Mazza, E. (2015) Deformation mechanisms of human amnion: quantitative studies based on second harmonic generation microscopy. J. Biomech. 48, 1606-1613. DOI: 10.1016/j.jbiomech.2015.01.045
[4] Weickenmeier J., Wu, R. Lecomte-Grosbras P., Witz, J.F., Brieu M., Winklhofer S., Andreisek G., Mazza E. (2014) Experimental Characterization and Simulation of Layer Interaction in Facial Soft Tissues. In: Bello F., Cotin S. (eds) Biomedical Simulation. ISBMS 2014. Lect. Notes Comp. Sci. 8789. DOI: 10.1007/978-3-319-12057-7_27
[5] Sachs D., Wahlsten, A., Kozerke S., Restivo G., Mazza E. (2021) A biphasic multilayer computational model of human skin. Biomech. Model. Mechanobiol. 20, 969-982. DOI: 10.1007/s10237-021-01424-w
[6] Domaschke S., Mechanical multiscale modelling of fibrous materials with application to electrospun networks. ETH Diss. Nr. 26328 (2019)
[7] Domaschke S., Morel A., Kaufmann R., Hofmann J., Rossi R.M., Mazza E., Fortunato G., Ehret A.E. (2020) Predicting the macroscopic response of electrospun membranes based on microstructure and single fibre properties. J. Mech. Behav. Biomed. Mater. 104, 103634. DOI: 10.1016/j.jmbbm.2020.103634
[8] Kuravi R. Investigating the role of meso-scale structure on the mechanical response of skeletal muscle tissues. ETH Diss. Nr. 27212 (2021)
[9] Kuravi R., Leichsenring K., Böl M., Ehret A.E. (2021) 3D finite element models from serial section histology of skeletal muscle tissue – The role of micro-architecture on mechanical behaviour. J. Mech. Behav. Biomed. Mater. 113, 104109 (2021). DOI: 10.1016/j.jmbbm.2020.104109
[10] Kuravi R., Leichsenring K., Trostorf R., Morales-Orcajo E., Böl M., Ehret A.E. (2021) Predicting muscle tissue response from calibrated component models and histology-based finite element models. J. Mech. Behav. Biomed. Mater. 117, 104375. DOI: 10.1016/j.jmbbm.2021.104375