The ever-increasing need for high contrast imaging solutions for molecular devices in biomedical systems warrants new technological developments. The modern technological revolution in high-content imaging along with high-content analysis provides attractive features to medical practitioners through operational flexibility and scalability. High content imaging is widely adopted for a variety of samples ranging from living cells to tissue slices including stem cells. Due to the revolution in stem cell therapy, more powerful and elegant tools are desired for cellular and molecular imaging. On the other hand, the imaging systems are to be supported by modern software tools with modularity to handle high content images that are characterized by great molecular data. The living cell images along with software modules for data analytics provide a quantum of cellular assays. These cellular assays increase the device throughput and enable medical personnel to arrive at new medical conclusions.

The advent of novel machine learning tools and algorithms enables the user to analyse a large volume of data and perform data prediction. Thus, the integration of high content imaging systems with machine learning tools will be a key enabler to improve data acquisition in sustainable and high precision molecular devices.

This Special Issue is focused to report the developments in high content imaging systems. The developments both in the hardware and software portions of cellular imaging systems are of interest. Furthermore, the dissemination of research interest in data analytics of high content imaging is also of interest. We welcome original research and review articles.

Potential topics include but are not limited to the following:

• Automated microscopic systems
• High content image acquisition systems
• High content image analysis techniques
• Resolution enhancement algorithms
• Statistical tools and data analytics in cellular imaging
• Cellular assay and 3D matrix creation and assessment
• Modularity in cellular imaging modes
• Modern machine learning techniques for cellular data analysis
• Optimization techniques
• Prediction algorithms for cellular imaging data