AIMC Topic: Microscopy, Phase-Contrast

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Automatic visual detection of activated sludge microorganisms based on microscopic phase contrast image optimisation and deep learning.

Journal of microscopy Jan 23, 2025
The types and quantities of microorganisms in activated sludge are directly related to the stability and efficiency of sewage treatment systems. This paper proposes a sludge microorganism detection method based on microscopic phase contrast image opt...
Algorithms Deep Learning Sewage Microscopy, Phase-Contrast Image Processing, Computer-Assisted Bacteria
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NeuroQuantify - An image analysis software for detection and quantification of neuron cells and neurite lengths using deep learning.

Journal of neuroscience methods Aug 27, 2024
BACKGROUND: The segmentation of cells and neurites in microscopy images of neuronal networks provides valuable quantitative information about neuron growth and neuronal differentiation, including the number of cells, neurites, neurite length and neur...
Neurites Microscopy, Phase-Contrast Animals Humans Deep Learning Image Processing, Computer-Assisted Software Neurons
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Deep learning-based segmentation of subcellular organelles in high-resolution phase-contrast images.

Cell structure and function Jul 31, 2024
Although quantitative analysis of biological images demands precise extraction of specific organelles or cells, it remains challenging in broad-field grayscale images, where traditional thresholding methods have been hampered due to complex image fea...
Image Processing, Computer-Assisted HeLa Cells Humans Deep Learning Microscopy, Phase-Contrast Organelles
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Machine learning approach for recognition and morphological analysis of isolated astrocytes in phase contrast microscopy.

Scientific reports Apr 29, 2024
Astrocytes are glycolytically active cells in the central nervous system playing a crucial role in various brain processes from homeostasis to neurotransmission. Astrocytes possess a complex branched morphology, frequently examined by fluorescent mic...
Microscopy, Phase-Contrast Cerebral Cortex Animals Machine Learning Image Processing, Computer-Assisted Rats Cells, Cultured Astrocytes
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Quality Control in the Corneal Bank with Artificial Intelligence: Comparison of a New Deep Learning-based Approach with Conventional Endothelial Cell Counting by the "Rhine-Tec Endothelial Analysis System".

Klinische Monatsblatter fur Augenheilkunde Apr 4, 2024
Endothelial cell density (ECD) is a crucial parameter for the release of corneal grafts for transplantation. The Lions Eye Bank of Baden-Württemberg uses the "Rhine-Tec Endothelial Analysis System" for ECD quantification, which is based on a fixed co...
Corneal Transplantation Eye Banks Microscopy, Phase-Contrast Endothelium, Corneal Artificial Intelligence Deep Learning Humans Cell Count Quality Control
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Label-free deep learning-based species classification of bacteria imaged by phase-contrast microscopy.

PLoS computational biology Nov 13, 2023
Reliable detection and classification of bacteria and other pathogens in the human body, animals, food, and water is crucial for improving and safeguarding public health. For instance, identifying the species and its antibiotic susceptibility is vita...
Humans Neural Networks, Computer Deep Learning Bacteria Bacterial Infections Microscopy, Phase-Contrast
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DeepSea is an efficient deep-learning model for single-cell segmentation and tracking in time-lapse microscopy.

Cell reports methods Jun 12, 2023
Time-lapse microscopy is the only method that can directly capture the dynamics and heterogeneity of fundamental cellular processes at the single-cell level with high temporal resolution. Successful application of single-cell time-lapse microscopy re...
Microscopy, Phase-Contrast Time-Lapse Imaging Deep Learning Microscopy
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Deep learning for asbestos counting.

Journal of hazardous materials May 6, 2023
The PCM (phase contrast microscopy) method for asbestos counting needs special sample treatments, hence it is time consuming and rather expensive. As an alternative, we implemented a deep learning procedure on images directly acquired from the untrea...
Occupational Exposure United States Asbestos, Serpentine Deep Learning Microscopy, Phase-Contrast Asbestos, Crocidolite Asbestos
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Investigating the robustness of a deep learning-based method for quantitative phase retrieval from propagation-based x-ray phase contrast measurements under laboratory conditions.

Physics in medicine and biology Apr 3, 2023
Quantitative phase retrieval (QPR) in propagation-based x-ray phase contrast imaging of heterogeneous and structurally complicated objects is challenging under laboratory conditions due to partial spatial coherence and polychromaticity. A deep learni...
Microscopy, Phase-Contrast Radiography X-Rays Deep Learning
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Phase Contrast Image Restoration by Formulating Its Imaging Principle and Reversing the Formulation With Deep Neural Networks.

IEEE transactions on medical imaging Apr 3, 2023
Phase contrast microscopy, as a noninvasive imaging technique, has been widely used to monitor the behavior of transparent cells without staining or altering them. Due to the optical principle of the specifically-designed microscope, phase contrast m...
Microscopy, Phase-Contrast Neural Networks, Computer Staining and Labeling Artifacts
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