University of Limassol: Breaking Barriers: Cloud Solutions Transform Cell Image Analysis

In scientific research, every discovery begins with a close examination of the tiniest details. Microscopy has long been a cornerstone of scientific enquiry. However, with the progress in imaging techniques and the advent of digital imaging, a new challenge has emerged – the need for sophisticated software to analyse these images effectively.

Traditionally, laboratories have grappled with the task of developing their own software solutions for cell image analysis, or purchasing third parties products. These approaches come with significant limitations. Firstly, the development and maintenance of such software require a dedicated team of skilled professionals, which can be both costly and time-consuming for smaller labs with limited resources. Moreover, keeping up with advancements in technology and incorporating new features into the software can be challenging for in-house development teams. Similarly, acquiring third-party solutions may be too expensive for smaller labs.

Cloud computing has the potential to address both these issues. The advantages of a cloud-based approach are manifold. Firstly, scalability is no longer a concern, as cloud platforms offer virtually unlimited computing resources on demand. This means that labs can scale their analysis capabilities seamlessly, whether they’re processing a handful of images or millions.

Economies of scale is another key advantage offered by cloud solutions. By leveraging shared infrastructure and resources, labs can significantly reduce costs compared to developing and maintaining their own software.

But what about the smaller labs, you may ask? Can they too benefit from the power of cloud-based microscopy image analysis? The answer is a resounding yes. In fact, cloud solutions level the playing field for smaller labs by providing access to state-of-the-art technologies that would otherwise be out of reach. This democratises access to advanced image analysis tools, making them accessible to labs of all sizes, regardless of their budget constraints.

The University of Limassol is working on a collaboration with the University of Cyprus for the development of a cloud native platform in which the power of cutting-edge technologies in advanced image processing, and deep learning, is harnessed to streamline the analysis of advanced experimental microscopy data. The project has the acronym ABiOMiCeC (i.e., Analysis of Biological Optical Microscopy Time-Lapse Video Sequences of Cells in the Cloud).

The platform is developed in Microsoft Azure, Microsoft offer for Cloud. We are leveraging several advanced cloud services such as Azure Data Factory for the migration and manipulation of data, Azure Synapse for scaling deep learning models capable of automatically identifying cells in images, and virtual machines to seamlessly scale the preprocessing of the images and the detection of cell motion and evolution through time. The project also uses Google Colab for ease of access.

Such analysis will enable biologists to gain better insights on several biological processes, such as the division of cancer cells, and follow their response to treatments.

The development of cloud solutions for microscopy image analysis represents a significant leap forward in scientific research. By offering unparallelled scalability and economies of scale, these solutions empower labs of all sizes to unlock the mysteries of the microscopic world like never before and pursue high-throughput studies for the development of drugs for the treatment of cancer and other conditions.

For more information for the project, you can read the article:

Symmetry | Free Full-Text | Spatiotemporal Identification of Cell Divisions Using Symmetry Properties in Time-Lapse Phase Contrast Microscopy (

The ABiOMiCeC project acknowledges the support of the Cyprus Research and Innovation Foundation within the context of the RESTART 2016-2020 Programmes, which are co-funded by national and European funds.

Source: University of Limassol | News (