End-to-End AI-Driven Microscopy Image Analysis Workflows for Cell Biology

About The Event

Modern light and electron microscopy enable the acquisition of highly complex, multidimensional datasets in cell biology. However, converting these data into reproducible and quantitative biological insights remains a significant challenge. Manual segmentation and measurement are often labor-intensive, subjective, and difficult to scale, creating analytical bottlenecks in research workflows.

This educational webinar presents an end-to-end, AI-driven image analysis workflow for quantitative cell biology. Following a brief overview of key challenges in microscopy data analysis, the session will examine the complete analytical pipeline, from data import to presentation of results.

We will discuss AI-enhanced pre-processing methods, including denoising, stack alignment, and membrane enhancement, aimed at improving image quality and analytical robustness. The webinar will then explore AI-based segmentation strategies, covering annotation workflows, model training, and automated structure detection using deep learning approaches such as Cellpose. Finally, we will address AI-supported quantification techniques, including classification, clustering, and visualization methods that facilitate scalable and reproducible biological interpretation.

Using representative examples from both light and electron microscopy, these workflows will be illustrated using Amira Software as an example platform for implementing AI-assisted image analysis in cell biology research.

Key Learnings:

• Understand the complete image analysis pipeline from data import to interpretation
• Explore best practices for pre-processing microscopy datasets for robust analysis
• Learn how AI-assisted segmentation reduces manual workload and variability
• Discover quantitative methods such as classification and clustering for biological interpretation