Reference : Basis of Image Analysis for Evaluating Cell Biomaterial Interaction Using Brightfield...
Scientific journals : Article
Scientific journals : Article
http://hdl.handle.net/10993/49656
Basis of Image Analysis for Evaluating Cell Biomaterial Interaction Using Brightfield Microscopy
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Uka, A. [Department of Computer Engineering, Epoka University, Tiranë, Albania]
Ndreu Halili, A. [Department of Computer Engineering, Epoka University, Tiranë, Albania, Department of Information Technology, Aleksandër Moisiu University, Durrës, Albania]
Polisi, X. [Department of Computer Engineering, Epoka University, Tiranë, Albania]
Topal, Ali Osman mailto [University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Computer Science (DCS)]
Imeraj, G. [Department of Computer Engineering, Epoka University, Tiranë, Albania]
Vrana, N. E. [Spartha Medical, Strasbourg, France, INSERM UMR 1121, Strasbourg, France]
2021
Cells Tissues Organs
S. Karger AG
210
2
77-104
Yes (verified by ORBilu)
14226405
[en] Biomaterial risk assessment ; Brightfield microscopy ; Cell detection ; Cytotoxicity ; Deep learning ; Microfluidic cells ; Review
[en] Medical imaging is a growing field that has stemmed from the need to conduct noninvasive diagnosis, monitoring, and analysis of biological systems. With the developments and advances in the medical field and the new techniques that are used in the intervention of diseases, very soon the prevalence of implanted biomedical devices will be even more significant. The implanted materials in a biological system are used in diverse fields, which require lengthy evaluation and validation processes. However, currently the evaluation of the toxicity of biomaterials has not been fully automated yet. Moreover, image analysis is an integral part of biomaterial research, but it is not within the core capacities of a significant portion of biomaterial scientists, which results in the use of predominantly ready-made tools. The detailed image analysis can be conducted once all the relevant parameters including the inherent characteristics of image acquisition techniques are considered. Herein, we cover the currently used image analysis-based techniques for assessment of biomaterial/cell interaction with a specific focus on unstained brightfield microscopy acquired mostly in but not limited to microfluidic systems, which serve as multiparametric sensing platforms for noninvasive experimental measurements. We present the major imaging acquisition techniques that enable point-of-care testing when incorporated with microfluidic cells, discuss the constraints enforced by the geometry of the system and the material that is analyzed, and the challenges that rise in the image analysis when unstained cell imaging is employed. Emerging techniques such as utilization of machine learning and cell-specific pattern recognition algorithms and potential future directions are discussed. Automation and optimization of biomaterial assessment can facilitate the discovery of novel biomaterials together with making the validation of biomedical innovations cheaper and faster. © 2021
Horizon 2020 Framework Programme, H2020: 760921
http://hdl.handle.net/10993/49656
10.1159/000512969

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