Advancements in Automated Image Analysis for Cell Imaging

Advances in Live Cell Imaging

Live cell imaging has made significant strides in recent years, becoming a powerful tool in cellular biology, microbiology, molecular biology, and drug discovery. Autonomous cell imaging systems, such as the WiScan Hermes 24/7, have revolutionized the field by simplifying and automating the process of imaging living cells. These cutting-edge systems enable researchers to continuously monitor cells in real-time, resulting in more accurate and reliable data. Such systems offer simple and fast tools for live cell analysis, including the automated detection of rare events occuring in live cells. It is an advanced solution that is perfectly geared towards solving the challenges of the growing live cell imaging market.

Deep Learning in Cell Image Analysis

Deep learning techniques, which are a form of AI (artificial intelligence), have been increasingly used in cell imaging software to extract valuable information about cells. By employing artificial intelligence and machine learning algorithms, these techniques enable automated image analysis pipelines to process and analyze vast amounts of cellular data more efficiently.

AI-based image analysis offers several benefits over conventional image analysis algorithms for the analysis of cell biology microscopy images. Firstly, AI-based algorithms can learn and adapt to complex biological structures and patterns, allowing for more accurate and reliable analysis of cellular features. Additionally, AI-based algorithms can process large amounts of data quickly, reducing the time and resources required for manual analysis. Furthermore, AI-based algorithms can identify subtle changes in cell morphology or behavior that may not be discernible with conventional analysis methods. Overall, the use of AI-based image analysis in cell biology microscopy can improve the efficiency and accuracy of data analysis, ultimately leading to better insights and discoveries in the field.

A recent example of the use of AI for analysis of microscopy images is found in IDEA Bio-Medical’s Athena Zebrafish software. Analyzing images of zebrafish is a time-consuming bottleneck in screening studies, and  while the images may be easily interpretable by a human, conventional image analysis algorithms do not readily detect the fish or its anatomy. Instead, measurements are performed manually using single images. IDEA Bio-Medical learned of this challenge from our clients, so we created a novel deep-learning AI  algorithm to automatically analyze their Zebrafish images for them.

The AI training process is complex and error-prone, requiring substantial time and effort that our biology-focused clients could not do themselves. The AI we have developed can now robustly identify objects like the zebrafish contour and its internal anatomy that are clearly visible to us humans. Athena permits parameter-free zebrafish analysis using simple bright-field images and it automatically detects zebrafish embryos and larvae up to 5 days old (dpf), extracting the fish contour and much of its internal anatomy (yolk sac, eye, notocord, and more), along with body regions of the head, trunk, and tail.

For each of these objects, the software measures the morphology (area, length, and shape) and can detect fluorescence in associated color channels. Both fluorescence intensity and spot/structure detection within specific anatomy are supported.The software is suited for a broad range of researchers and accepts multiple image format types output from nearly all microscope manufacturers.

Image Segmentation and Analysis

Image segmentation is a crucial aspect of automated image analysis, allowing for greater understanding of cellular information. Researchers have developed novel solutions for cell segmentation in imaging-based spatial transcriptomics, enabling the separation and analysis of complex cellular structures, including cell clumps and cellular layers. These advancements have significantly improved the accuracy and efficiency of cell imaging systems and have allowed for a more comprehensive analysis of cellular events.

Integration of Cellular Imaging Techniques

The integration of various cellular imaging techniques has led to the development of multiplexed imaging platforms that streamline the research process. One such example is the WiSoft® Athena image analysis software, which is specifically designed to analyze the results of experiments conducted using multiplexed imaging platforms.

Multiplexed imaging platforms employ fluorescence microscopy to capture and analyze multiple images simultaneously, enabling researchers to investigate numerous cellular markers and processes concurrently. High content imaging (HCI) is a prime example of multiplexed imaging, extensively utilized in cellular biology, microbiology, molecular biology, and drug discovery.

These platforms are also highly valuable for analyzing spheroids and Organoids, which are three-dimensional cell cultures that have gained significant importance in drug discovery research. WiSoft® Athena’s sophisticated image analysis capabilities offer researchers a more comprehensive understanding of cellular dynamics and interactions, ultimately enhancing the efficiency and accuracy of their work.

Looking for Cell Imaging Solutions?

At IDEA-Bio, we understand the critical role that advanced cell imaging plays in driving scientific research forward. Our team of experts is dedicated to providing cutting-edge cell imaging technology and support, ensuring that you have access to the latest innovations in automated image analysis for cell imaging.

Whether you’re interested in live cell imaging, single-cell analysis, or cellular imaging techniques, we can help you find the right solution to meet your research needs. With access to state-of-the-art cell imaging microscope systems, cell imaging software and lab automation, IDEA-Bio is your one-stop shop for all your cell imaging requirements.

As the cell imaging market continues to grow and evolve, we are committed to staying at the forefront of this exciting field. Contact us today to learn more about how our cell imaging solutions can help advance your research and bring new discoveries to light.

References and further reding

1. https://www.genengnews.com/resources/ebooks/advances-in-live-cell-biological-image-analysis 
2. Xu Junde, Zhou Donghao, Deng Danruo, Li Jingpeng, Chen Cheng, Liao Xiangyun, Chen Guangyong, Heng Pheng Ann. Deep Learning in Cell Image Analysis. Intell Comput. 2022:2022;DOI:10.34133/2022/9861263
3. Shengyong Chen, Mingzhu Zhao, Guang Wu, Chunyan Yao, Jianwei Zhang, “Recent Advances in Morphological Cell Image Analysis”, Computational and Mathematical Methods in Medicine, vol. 2012, Article ID 101536, 10 pages, 2012. https://doi.org/10.1155/2012/101536