AI Medical Compendium Topic

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Microscopy, Electron

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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 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 ...

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 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...

Instance segmentation of mitochondria in electron microscopy images with a generalist deep learning model trained on a diverse dataset.

Cell systems
Mitochondria are extremely pleomorphic organelles. Automatically annotating each one accurately and precisely in any 2D or volume electron microscopy (EM) image is an unsolved computational challenge. Current deep learning-based approaches train mode...

Integrating Molecular Models Into CryoEM Heterogeneity Analysis Using Scalable High-resolution Deep Gaussian Mixture Models.

Journal of molecular biology
Resolving the structural variability of proteins is often key to understanding the structure-function relationship of those macromolecular machines. Single particle analysis using Cryogenic electron microscopy (CryoEM), combined with machine learning...

Mesoscopic structural analysis via deep learning processing, with a special reference to in vitro alteration in collagen fibre induced by a gap junction inhibitor.

Microscopy (Oxford, England)
Dense connective tissue, including the ligament, tendon, fascia and cornea, is formed by regularly arranged collagen fibres synthesized by fibroblasts (Fbs). The mechanism by which fibre orientation is determined remains unclear. Periodontal ligament...

Deep learning-based multi-model approach on electron microscopy image of renal biopsy classification.

BMC nephrology
BACKGROUND: Electron microscopy is important in the diagnosis of renal disease. For immune-mediated renal disease diagnosis, whether the electron-dense granule is present in the electron microscope image is of vital importance. Deep learning methods ...

Segmentation in large-scale cellular electron microscopy with deep learning: A literature survey.

Medical image analysis
Electron microscopy (EM) enables high-resolution imaging of tissues and cells based on 2D and 3D imaging techniques. Due to the laborious and time-consuming nature of manual segmentation of large-scale EM datasets, automated segmentation approaches a...

Adaptive Ensemble Refinement of Protein Structures in High Resolution Electron Microscopy Density Maps with Radical Augmented Molecular Dynamics Flexible Fitting.

Journal of chemical information and modeling
Recent advances in cryo-electron microscopy (cryo-EM) have enabled modeling macromolecular complexes that are essential components of the cellular machinery. The density maps derived from cryo-EM experiments are often integrated with manual, knowledg...