AIMC Topic: Microscopy, Confocal

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Machine-learning assisted confocal imaging of intracellular sites of triglycerides and cholesteryl esters formation and storage.

Analytica chimica acta
All living systems are maintained by a constant flux of metabolic energy and, among the different reactions, the process of lipids storage and lipolysis is of fundamental importance. Current research has focused on the investigation of lipid droplets...

Technological advances for the detection of melanoma: Advances in diagnostic techniques.

Journal of the American Academy of Dermatology
Managing the balance between accurately identifying early stage melanomas while avoiding obtaining biopsy specimens of benign lesions (ie, overbiopsy) is the major challenge of melanoma detection. Decision making can be especially difficult in patien...

Classifying changes in LN-18 glial cell morphology: a supervised machine learning approach to analyzing cell microscopy data via FIJI and WEKA.

Medical & biological engineering & computing
In cell-based research, the process of visually monitoring cells generates large image datasets that need to be evaluated for quantifiable information in order to track the effectiveness of treatments in vitro. With the traditional, end-point assay-b...

Deep principal dimension encoding for the classification of early neoplasia in Barrett's Esophagus with volumetric laser endomicroscopy.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Barrett cancer is a treatable disease when detected at an early stage. However, current screening protocols are often not effective at finding the disease early. Volumetric Laser Endomicroscopy (VLE) is a promising new imaging tool for finding dyspla...

Machine Learning-Based Classification of the Health State of Mice Colon in Cancer Study from Confocal Laser Endomicroscopy.

Scientific reports
In this article, we address the problem of the classification of the health state of the colon's wall of mice, possibly injured by cancer with machine learning approaches. This problem is essential for translational research on cancer and is a priori...

Utilizing Machine Learning for Image Quality Assessment for Reflectance Confocal Microscopy.

The Journal of investigative dermatology
In vivo reflectance confocal microscopy (RCM) enables clinicians to examine lesions' morphological and cytological information in epidermal and dermal layers while reducing the need for biopsies. As RCM is being adopted more widely, the workflow is e...

Robust blind spectral unmixing for fluorescence microscopy using unsupervised learning.

PloS one
Due to the overlapping emission spectra of fluorophores, fluorescence microscopy images often have bleed-through problems, leading to a false positive detection. This problem is almost unavoidable when the samples are labeled with three or more fluor...

An artificial intelligence-based deep learning algorithm for the diagnosis of diabetic neuropathy using corneal confocal microscopy: a development and validation study.

Diabetologia
AIMS/HYPOTHESIS: Corneal confocal microscopy is a rapid non-invasive ophthalmic imaging technique that identifies peripheral and central neurodegenerative disease. Quantification of corneal sub-basal nerve plexus morphology, however, requires either ...

Computer-assisted assessment of colonic polyp histopathology using probe-based confocal laser endomicroscopy.

International journal of colorectal disease
INTRODUCTION: Probe-based confocal laser endomicroscopy (pCLE) is a promising modality for classifying polyp histology in vivo, but decision making in real-time is hampered by high-magnification targeting and by the learning curve for image interpret...