AIMC Topic: Microscopy, Electron

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Automated cell structure extraction for 3D electron microscopy by deep learning.

Scientific reports
Modeling the 3D structures of cells and tissues is crucial in biology. Sequential cross-sectional images from electron microscopy provide high-resolution intracellular structure information. The segmentation of complex cell structures remains a labor...

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...

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...

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...

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...

Generative and discriminative model-based approaches to microscopic image restoration and segmentation.

Microscopy (Oxford, England)
Image processing is one of the most important applications of recent machine learning (ML) technologies. Convolutional neural networks (CNNs), a popular deep learning-based ML architecture, have been developed for image processing applications. Howev...

Practical method of cell segmentation in electron microscope image stack using deep convolutional neural network☆.

Microscopy (Oxford, England)
Segmentation of three-dimensional (3D) electron microscopy (EM) image stacks is an arduous and tedious task. Deep convolutional neural networks (CNNs) work well to automate the segmentation; however, they require a large training dataset, which is a ...

An Effective Encoder-Decoder Network for Neural Cell Bodies and Cell Nucleus Segmentation of EM Images.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Neural systems are complicated networks connected by a large number of neurons through gap junctions and synapse. At present, for electron microscopy connectomics research, neuron structure recognition algorithms mostly focus on synapses, dendrites, ...

Automatic Classification for the Type of Multiple Synapse Based on Deep Learning.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Recent studies have shown that the synaptic plasticity induced by development and learning can promote the formation of multiple synapse. With the rapid development of electron microscopy (EM) technology, we can closely observe the multiple synapse s...

Machine Learning: Advanced Image Segmentation Using ilastik.

Methods in molecular biology (Clifton, N.J.)
Segmentation is one of the most ubiquitous problems in biological image analysis. Here we present a machine learning-based solution to it as implemented in the open source ilastik toolkit. We give a broad description of the underlying theory and demo...