Contributions of deep learning to automated numerical modelling of the interaction of electric fields and cartilage tissue based on 3D images

Frontiers in Bioengineering and Biotechnology

Bioengineering and Biotechnology

X. Lucas Lu¹  Yilu Zhou¹  Vien Lam Che²  Julius Zimmermann²  Ursula van Rienen³ 

[1] Department of Mechanical Engineering, University of Delaware, Delaware, DE, United States;Institute of General Electrical Engineering, University of Rostock, Rostock, Germany;Institute of General Electrical Engineering, University of Rostock, Rostock, Germany;Department Life, Light and Matter, University of Rostock, Rostock, Germany;Department of Ageing of Individuals and Society, Interdisciplinary Faculty, University of Rostock, Rostock, Germany;

关键词: machine learning;    deep learning;    image segmentation;    bioimpedance;    numerical models;    electrical stimulation;    computational electromagnetics;   

DOI  :  10.3389/fbioe.2023.1225495

 received in 2023-05-19, accepted in 2023-08-07,  发布年份 2023

来源: Frontiers

【 摘 要 】

Electric fields find use in tissue engineering but also in sensor applications besides the broad classical application range. Accurate numerical models of electrical stimulation devices can pave the way for effective therapies in cartilage regeneration. To this end, the dielectric properties of the electrically stimulated tissue have to be known. However, knowledge of the dielectric properties is scarce. Electric field-based methods such as impedance spectroscopy enable determining the dielectric properties of tissue samples. To develop a detailed understanding of the interaction of the employed electric fields and the tissue, fine-grained numerical models based on tissue-specific 3D geometries are considered. A crucial ingredient in this approach is the automated generation of numerical models from biomedical images. In this work, we explore classical and artificial intelligence methods for volumetric image segmentation to generate model geometries. We find that deep learning, in particular the StarDist algorithm, permits fast and automatic model geometry and discretisation generation once a sufficient amount of training data is available. Our results suggest that already a small number of 3D images (23 images) is sufficient to achieve 80% accuracy on the test data. The proposed method enables the creation of high-quality meshes without the need for computer-aided design geometry post-processing. Particularly, the computational time for the geometrical model creation was reduced by half. Uncertainty quantification as well as a direct comparison between the deep learning and the classical approach reveal that the numerical results mainly depend on the cell volume. This result motivates further research into impedance sensors for tissue characterisation. The presented approach can significantly improve the accuracy and computational speed of image-based models of electrical stimulation for tissue engineering applications.

【 授权许可】

Unknown   
Copyright © 2023 Che, Zimmermann, Zhou, Lu and van Rienen.