Automated segmentation of cell organelles in volume electron microscopy using deep learning.

Recent advances in computing power triggered the use of artificial intelligence in image analysis in life sciences. To train these algorithms, a large enough set of certified labeled data is required. The trained neural network is then capable of producing accurate instance segmentation results that will then need to be re-assembled into the original dataset: the entire process requires substantial expertise and time to achieve quantifiable results.

Volume electron microscopy.

Life exists in three dimensions, but until the turn of the century most electron microscopy methods provided only 2D image data. Recently, electron microscopy techniques capable of delving deep into the structure of cells and tissues have emerged, collectively called volume electron microscopy (vEM). Developments in vEM have been dubbed a quiet revolution as the field evolved from established transmission and scanning electron microscopy techniques, so early publications largely focused on the bioscience applications rather than the underlying technological breakthroughs.

Femtosecond laser preparation of resin embedded samples for correlative microscopy workflows in life sciences.

Correlative multimodal imaging is a useful approach to investigate complex structural relations in life sciences across multiple scales. For these experiments, sample preparation workflows that are compatible with multiple imaging techniques must be established. In one such implementation, a fluorescently labeled region of interest in a biological soft tissue sample can be imaged with light microscopy before staining the specimen with heavy metals, enabling follow-up higher resolution structural imaging at the targeted location, bringing context where it is required.

Volume EM: a quiet revolution takes shape.

Volume Electron Microscopy is a group of techniques that reveal the 3D ultrastructure of cells and tissues through volumes greater than 1 cubic micron. A burgeoning grass roots community effort is fast building the profile, and revealing the impact, of vEM technology in the life sciences and clinical research.