RetFM-J, an ImageJ-based module for automated counting and quantifying features of nuclei in retinal whole-mounts.

Exp Eye Res

VA Center for the Prevention and Treatment of Visual Loss, Iowa City VA Health Care System, Iowa City, IA, USA; Department of Molecular Physiology and Biophysics, University of Iowa, Iowa City, IA 52242, USA; Department of Ophthalmology and Visual Sciences, University of Iowa, Iowa City, IA 52242, USA. Electronic address:

Published: May 2016

The present article introduces RetFM-J, a semi-automated ImageJ-based module that detects, counts, and collects quantitative data on nuclei of the inner retina from H&E-stained whole-mounted retinas. To illustrate performance, computer-derived outputs were analyzed in inbred C57BL/6J mice. Automated characterization yielded computer-derived outputs that closely matched manual counts. As a method using open-source software that is freely available, inexpensive staining reagents that are robust, and imaging equipment that is routine to most laboratories, RetFM-J could be utilized in a wide variety of experiments benefiting from high-throughput, quantitative, uniform analyses of total cellularity in the inner retina.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4753132PMC
http://dx.doi.org/10.1016/j.exer.2015.07.020DOI Listing

Publication Analysis

Top Keywords

imagej-based module
8
inner retina
8
computer-derived outputs
8
retfm-j imagej-based
4
module automated
4
automated counting
4
counting and quantifying
4
and quantifying features
4
features nuclei
4
nuclei retinal
4

Similar Publications

Integrative Toolkit to Analyze Cellular Signals: Forces, Motion, Morphology, and Fluorescence.

J Vis Exp

March 2022

Center for Lung Biology, College of Medicine, University of South Alabama; William B. Burnsed Jr. Mechanical, Aerospace, and Biomedical Engineering Department, College of Engineering, University of South Alabama; Department of Pharmacology, College of Medicine, University of South Alabama;

Quantitative assessment of cellular forces and motion advanced considerably over the last four decades. These advancements provided the framework to examine insightful mechanical signaling processes in cell culture systems. However, the field currently faces three problems: lack of quality standardization of the acquired data, technical errors in data analysis and visualization, and perhaps most importantly, the technology remains largely out of reach for common cell biology laboratories.

View Article and Find Full Text PDF

Purpose: The purpose of this study was to investigate the use of anterior segment imaging in diagnosing Kayser-Fleischer rings in patients with Wilson disease.

Methods: In a tertiary center for Wilson disease, patients were examined with a Pentacam HR Scheimpflug-based tomography device in addition to conventional slit-lamp examination. The inferior part of the cornea was analyzed using both a built-in densitometry module and ImageJ.

View Article and Find Full Text PDF

Quantitative measurement of retinal ganglion cell populations via histology-based random forest classification.

Exp Eye Res

May 2016

VA Center for the Prevention and Treatment of Visual Loss, Iowa City VA Health Care System, Iowa City, IA, USA; Department of Molecular Physiology and Biophysics, University of Iowa, Iowa City, IA 52242, USA; Department of Ophthalmology and Visual Sciences, University of Iowa, Iowa City, IA 52242, USA. Electronic address:

The inner surface of the retina contains a complex mixture of neurons, glia, and vasculature, including retinal ganglion cells (RGCs), the final output neurons of the retina and primary neurons that are damaged in several blinding diseases. The goal of the current work was two-fold: to assess the feasibility of using computer-assisted detection of nuclei and random forest classification to automate the quantification of RGCs in hematoxylin/eosin (H&E)-stained retinal whole-mounts; and if possible, to use the approach to examine how nuclear size influences disease susceptibility among RGC populations. To achieve this, data from RetFM-J, a semi-automated ImageJ-based module that detects, counts, and collects quantitative data on nuclei of H&E-stained whole-mounted retinas, were used in conjunction with a manually curated set of images to train a random forest classifier.

View Article and Find Full Text PDF

RetFM-J, an ImageJ-based module for automated counting and quantifying features of nuclei in retinal whole-mounts.

Exp Eye Res

May 2016

VA Center for the Prevention and Treatment of Visual Loss, Iowa City VA Health Care System, Iowa City, IA, USA; Department of Molecular Physiology and Biophysics, University of Iowa, Iowa City, IA 52242, USA; Department of Ophthalmology and Visual Sciences, University of Iowa, Iowa City, IA 52242, USA. Electronic address:

The present article introduces RetFM-J, a semi-automated ImageJ-based module that detects, counts, and collects quantitative data on nuclei of the inner retina from H&E-stained whole-mounted retinas. To illustrate performance, computer-derived outputs were analyzed in inbred C57BL/6J mice. Automated characterization yielded computer-derived outputs that closely matched manual counts.

View Article and Find Full Text PDF

Especially for investigator-initiated research at universities and academic institutions, Internet-based rare disease registries (RDR) are required that integrate electronic data capture (EDC) with automatic image analysis or manual image annotation. We propose a modular framework merging alpha-numerical and binary data capture. In concordance with the Office of Rare Diseases Research recommendations, a requirement analysis was performed based on several RDR databases currently hosted at Uniklinik RWTH Aachen, Germany.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!