AIMC Topic: Microscopy, Electron

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Deep neural network automated segmentation of cellular structures in volume electron microscopy.

The Journal of cell biology
Volume electron microscopy is an important imaging modality in contemporary cell biology. Identification of intracellular structures is a laborious process limiting the effective use of this potentially powerful tool. We resolved this bottleneck with...

Machine learning in electron microscopy for advanced nanocharacterization: current developments, available tools and future outlook.

Nanoscale horizons
In the last few years, electron microscopy has experienced a new methodological paradigm aimed to fix the bottlenecks and overcome the challenges of its analytical workflow. Machine learning and artificial intelligence are answering this call providi...

Deep learning-based synapse counting and synaptic ultrastructure analysis of electron microscopy images.

Journal of neuroscience methods
BACKGROUND: Synapses are the connections between neurons in the central nervous system (CNS) or between neurons and other excitable cells in the peripheral nervous system (PNS), where electrical or chemical signals rapidly travel through one cell to ...

DeepContact: High-throughput quantification of membrane contact sites based on electron microscopy imaging.

The Journal of cell biology
Membrane contact site (MCS)-mediated organelle interactions play essential roles in the cell. Quantitative analysis of MCSs reveals vital clues for cellular responses under various physiological and pathological conditions. However, an efficient tool...

Deep learning based domain adaptation for mitochondria segmentation on EM volumes.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Accurate segmentation of electron microscopy (EM) volumes of the brain is essential to characterize neuronal structures at a cell or organelle level. While supervised deep learning methods have led to major breakthroughs in ...

Hypothesis Learning in Automated Experiment: Application to Combinatorial Materials Libraries.

Advanced materials (Deerfield Beach, Fla.)
Machine learning is rapidly becoming an integral part of experimental physical discovery via automated and high-throughput synthesis, and active experiments in scattering and electron/probe microscopy. This, in turn, necessitates the development of a...

Electron microscopy of cardiac 3D nanodynamics: form, function, future.

Nature reviews. Cardiology
The 3D nanostructure of the heart, its dynamic deformation during cycles of contraction and relaxation, and the effects of this deformation on cell function remain largely uncharted territory. Over the past decade, the first inroads have been made to...

Method for quantifying the reaction degree of slag in alkali-activated cements using deep learning-based electron microscopy image analysis.

Journal of microscopy
In this paper, we present a methodology for measuring the reaction degree of ground granulated blast furnace slag (GGBFS) in alkali-activated cements using neural network based image analysis. The new methodology consists of an image analysis routine...

Stable Deep Neural Network Architectures for Mitochondria Segmentation on Electron Microscopy Volumes.

Neuroinformatics
Electron microscopy (EM) allows the identification of intracellular organelles such as mitochondria, providing insights for clinical and scientific studies. In recent years, a number of novel deep learning architectures have been published reporting ...

CleftNet: Augmented Deep Learning for Synaptic Cleft Detection From Brain Electron Microscopy.

IEEE transactions on medical imaging
Detecting synaptic clefts is a crucial step to investigate the biological function of synapses. The volume electron microscopy (EM) allows the identification of synaptic clefts by photoing EM images with high resolution and fine details. Machine lear...