Basis of Image Analysis for Evaluating Cell Biomaterial Interaction Using Brightfield Microscopy.

Area of Science:

• Biomaterials Science
• Medical Imaging
• Cell Biology

Background:

• Medical imaging is crucial for noninvasive diagnosis and monitoring, with increasing use of implanted biomedical devices.
• Current biomaterial toxicity evaluation lacks full automation, and many scientists lack advanced image analysis expertise.
• Microfluidic systems offer multiparametric sensing for noninvasive measurements, integrating imaging for point-of-care testing.

Purpose of the Study:

• To review image analysis techniques for assessing biomaterial/cell interactions, focusing on unstained brightfield microscopy in microfluidic systems.
• To discuss imaging acquisition techniques, system constraints, and challenges in analyzing unstained cell images.
• To explore emerging methods like machine learning for automated biomaterial assessment.

Main Methods:

• Review of current image analysis-based techniques for biomaterial/cell interaction assessment.
• Focus on unstained brightfield microscopy, particularly within microfluidic systems.
• Discussion of imaging acquisition, system constraints, and analysis challenges.

Main Results:

• Detailed analysis of image acquisition techniques for microfluidic-based point-of-care testing.
• Identification of constraints from system geometry and material properties affecting image analysis.
• Highlighting challenges in analyzing unstained cell images, necessitating advanced algorithms.

Conclusions:

• Automation and optimization of biomaterial assessment through advanced image analysis are critical.
• Machine learning and pattern recognition offer promising future directions for faster, cheaper validation of biomedical innovations.
• Improved image analysis can significantly facilitate the discovery of novel biomaterials and streamline biomedical innovation validation.