AIMC Topic: Microscopy, Confocal

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Nuquantus: Machine learning software for the characterization and quantification of cell nuclei in complex immunofluorescent tissue images.

Scientific reports
Determination of fundamental mechanisms of disease often hinges on histopathology visualization and quantitative image analysis. Currently, the analysis of multi-channel fluorescence tissue images is primarily achieved by manual measurements of tissu...

Detection of Dendritic Spines Using Wavelet-Based Conditional Symmetric Analysis and Regularized Morphological Shared-Weight Neural Networks.

Computational and mathematical methods in medicine
Identification and detection of dendritic spines in neuron images are of high interest in diagnosis and treatment of neurological and psychiatric disorders (e.g., Alzheimer's disease, Parkinson's diseases, and autism). In this paper, we have proposed...

Unsupervised lineage-based characterization of primate precursors reveals high proliferative and morphological diversity in the OSVZ.

The Journal of comparative neurology
Generation of the primate cortex is characterized by the diversity of cortical precursors and the complexity of their lineage relationships. Recent studies have reported miscellaneous precursor types based on observer classification of cell biology f...

Quantification of whey in fluid milk using confocal Raman microscopy and artificial neural network.

Journal of dairy science
In this work, we assessed the use of confocal Raman microscopy and artificial neural network as a practical method to assess and quantify adulteration of fluid milk by addition of whey. Milk samples with added whey (from 0 to 100%) were prepared, sim...

Three-Dimensional Visualisation of Blood Vessels in Human Gliomas Using Tissue Clearing and Deep Learning.

Neuropathology and applied neurobiology
Gliomas, with their intricate and aggressive nature, call for a detailed visualisation of their vasculature. Traditional 2D imaging often overlooks the spatial heterogeneity of tumours. Our study overcomes this by combining tissue clearing, 3D-confoc...

Domain-specific AI segmentation of IMPDH2 rod/ring structures in mouse embryonic stem cells.

BMC biology
BACKGROUND: Inosine monophosphate dehydrogenase 2 (IMPDH2) is an enzyme that catalyses the rate-limiting step of guanine nucleotides. In mouse embryonic stem cells (ESCs), IMPDH2 forms large multi-protein complexes known as rod-ring (RR) structures t...

Artificial intelligence-aided optical biopsy improves the diagnosis of esophageal squamous neoplasm.

World journal of gastroenterology
BACKGROUND: Early detection of esophageal squamous neoplasms (ESN) is essential for improving patient prognosis. Optical diagnosis of ESN remains challenging. Probe-based confocal laser endomicroscopy (pCLE) enables accurate histological observation...

Artificial intelligence model for the assessment of unstained live sperm morphology.

Reproduction & fertility
ABSTRACT: Traditional sperm morphology assessment requires staining and high magnification (100×), rendering sperm unsuitable for further use. We aimed to determine whether an in-house artificial intelligence (AI) model could reliably assess normal s...

[Application of neural networks for improving the methods of assessment of corneal nerve fibers (preliminary report)].

Vestnik oftalmologii
UNLABELLED: Processing large datasets using artificial intelligence is a promising approach in disease diagnosis and monitoring that focuses on improving research algorithms for existing technologies. Interest in studying corneal nerve fibers (CNFs) ...

A framework of multi-view machine learning for biological spectral unmixing of fluorophores with overlapping excitation and emission spectra.

Briefings in bioinformatics
The accuracy of assigning fluorophore identity and abundance, known as spectral unmixing, in biological fluorescence microscopy images remains a significant challenge due to the substantial overlap in emission spectra among fluorophores. In tradition...