Advanced Post-Processing Techniques for Electron Microscopy Data

This course provides an in-depth exploration of advanced computational techniques used to analyze electron microscopy (EM) datasets. Participants will learn to identify certain subcellular structures and how to characterize them with quantitative methods, with a particular focus on extracellular vesicles (EVs) and lipid droplets (LDs). Emphasis is placed on post-processing workflows including image enhancement, segmentation, and feature extraction. The course also introduces best practices for data annotation and prepares students to integrate EM data into machine learning frameworks, enabling automated classification and pattern recognition. Designed for researchers in bioimaging and life sciences, this course bridges microscopy, data science, and machine learning to support cutting-edge biological research.