AI Medical Compendium Topic

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Microscopy, Electron

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Decoding the microstructural properties of white matter using realistic models.

NeuroImage
Multi-echo gradient echo (ME-GRE) magnetic resonance signal evolution in white matter has a strong dependence on the orientation of myelinated axons with respect to the main static field. Although analytical solutions have been able to predict some o...

Three-Dimensional Visualization of the Podocyte Actin Network Using Integrated Membrane Extraction, Electron Microscopy, and Machine Learning.

Journal of the American Society of Nephrology : JASN
BACKGROUND: Actin stress fibers are abundant in cultured cells, but little is known about them . In podocytes, much evidence suggests that mechanobiologic mechanisms underlie podocyte shape and adhesion in health and in injury, with structural change...

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...

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 ...

Applications of deep learning in electron microscopy.

Microscopy (Oxford, England)
We review the growing use of machine learning in electron microscopy (EM) driven in part by the availability of fast detectors operating at kiloHertz frame rates leading to large data sets that cannot be processed using manually implemented algorithm...

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...

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...

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...

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 ...

Fear memory-associated synaptic and mitochondrial changes revealed by deep learning-based processing of electron microscopy data.

Cell reports
Serial section electron microscopy (ssEM) can provide comprehensive 3D ultrastructural information of the brain with exceptional computational cost. Targeted reconstruction of subcellular structures from ssEM datasets is less computationally demandin...