Department of Computer Engineering, Epoka University, Tiranë, Albania.
Department of Information Technology, Aleksandër Moisiu University, Durrës, Albania.
Cells Tissues Organs. 2021;210(2):77-104. doi: 10.1159/000512969. Epub 2021 Jun 29.
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.
医学成像领域发展迅速，它源自对生物系统进行非侵入性诊断、监测和分析的需求。随着医学领域的发展和进步以及疾病干预中新技术的应用，植入式生物医学设备的普及程度将会更高。生物系统中使用的植入材料应用于多个领域，这需要进行漫长的评估和验证过程。然而，目前生物材料的毒性评估尚未完全实现自动化。此外，图像分析是生物材料研究的一个组成部分，但并不是大部分生物材料科学家的核心能力范围之内，这导致他们主要使用现成的工具。只有在考虑了所有相关参数（包括图像采集技术的固有特性）后，才能进行详细的图像分析。本文涵盖了目前用于评估生物材料/细胞相互作用的基于图像分析的技术，特别关注未经染色的明场显微镜，这些技术主要应用于微流控系统中，但不仅限于此，微流控系统可作为用于非侵入性实验测量的多参数传感平台。我们介绍了主要的成像采集技术，当它们与微流控细胞结合使用时，可以实现即时护理测试，讨论了系统几何形状和被分析材料施加的限制，以及在使用未经染色的细胞成像时图像分析中出现的挑战。还讨论了新兴技术，如机器学习和细胞特异性模式识别算法的利用，以及潜在的未来发展方向。生物材料评估的自动化和优化可以促进新型生物材料的发现，并使生物医学创新的验证更加经济和快速。
