EM Image

Electron microscopy (EM) image analysis focuses on extracting meaningful information from high-resolution images, often addressing challenges like noise, low contrast, and the need for large-scale 3D reconstructions. Current research heavily utilizes deep learning, employing architectures such as convolutional neural networks (CNNs), autoencoders, and recurrent networks like ConvLSTMs, to improve image super-resolution, segmentation, and correlative analysis with other imaging modalities like light microscopy. These advancements enable more accurate and efficient analysis of biological structures (e.g., chromosomes, microorganisms) and contribute significantly to fields like neuroscience, cytogenetics, and environmental science. Improved image processing techniques are crucial for automating analysis and extracting biologically relevant information from complex EM datasets.