AIMC Topic: Microscopy, Confocal

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Astrocyte regional heterogeneity revealed through machine learning-based glial neuroanatomical assays.

The Journal of comparative neurology
Evaluation of reactive astrogliosis by neuroanatomical assays represents a common experimental outcome for neuroanatomists. The literature demonstrates several conflicting results as to the accuracy of such measures. We posited that the diverging res...

Deep learning systems detect dysplasia with human-like accuracy using histopathology and probe-based confocal laser endomicroscopy.

Scientific reports
Probe-based confocal laser endomicroscopy (pCLE) allows for real-time diagnosis of dysplasia and cancer in Barrett's esophagus (BE) but is limited by low sensitivity. Even the gold standard of histopathology is hindered by poor agreement between path...

High performance in risk stratification of intraductal papillary mucinous neoplasms by confocal laser endomicroscopy image analysis with convolutional neural networks (with video).

Gastrointestinal endoscopy
BACKGROUND AND AIMS: EUS-guided needle-based confocal laser endomicroscopy (EUS-nCLE) can differentiate high-grade dysplasia/adenocarcinoma (HGD-Ca) in intraductal papillary mucinous neoplasms (IPMNs) but requires manual interpretation. We sought to ...

A deep learning approach for staging embryonic tissue isolates with small data.

PloS one
Machine learning approaches are becoming increasingly widespread and are now present in most areas of research. Their recent surge can be explained in part due to our ability to generate and store enormous amounts of data with which to train these mo...

Segmentation of cellular patterns in confocal images of melanocytic lesions in vivo via a multiscale encoder-decoder network (MED-Net).

Medical image analysis
In-vivo optical microscopy is advancing into routine clinical practice for non-invasively guiding diagnosis and treatment of cancer and other diseases, and thus beginning to reduce the need for traditional biopsy. However, reading and analysis of the...

A Deep Learning Model for Automated Sub-Basal Corneal Nerve Segmentation and Evaluation Using In Vivo Confocal Microscopy.

Translational vision science & technology
PURPOSE: The purpose of this study was to establish a deep learning model for automated sub-basal corneal nerve fiber (CNF) segmentation and evaluation with in vivo confocal microscopy (IVCM).

Automated Recognition of Retinal Pigment Epithelium Cells on Limited Training Samples Using Neural Networks.

Translational vision science & technology
PURPOSE: To develop a neural network (NN)-based approach, with limited training resources, that identifies and counts the number of retinal pigment epithelium (RPE) cells in confocal microscopy images obtained from cell culture or mice RPE/choroid fl...

Learning from irregularly sampled data for endomicroscopy super-resolution: a comparative study of sparse and dense approaches.

International journal of computer assisted radiology and surgery
PURPOSE: Probe-based confocal laser endomicroscopy (pCLE) enables performing an optical biopsy via a probe. pCLE probes consist of multiple optical fibres arranged in a bundle, which taken together generate signals in an irregularly sampled pattern. ...

Machine learning-assisted neurotoxicity prediction in human midbrain organoids.

Parkinsonism & related disorders
INTRODUCTION: Brain organoids are highly complex multi-cellular tissue proxies, which have recently risen as novel tools to study neurodegenerative diseases such as Parkinson's disease (PD). However, with increasing complexity of the system, usage of...