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Super-resolution Fluorescence Microscopy

Super-resolution Fluorescence Microscopy

Super-resolution fluorescence microscopy (SRFM) provides a better resolution than conventional fluorescence microscopy by reducing the point spread function (PSF). PSF is the light intensity distribution from a point that causes it to appear blurred. Due to PSF, each fluorescing point appears bigger than its actual size, and it is the PSF interference of nearby fluorophores that causes the blurred image. Various approaches to achieving higher resolution through SRFM have recently been...

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Updated: Jul 3, 2025

Retrograde Fluorescent Labeling Allows for Targeted Extracellular Single-unit Recording from Identified Neurons In vivo

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Published on: June 26, 2013

Fluorescent Neuronal Cells v2: multi-task, multi-format annotations for deep learning in microscopy.

Luca Clissa^1,2, Antonio Macaluso³, Roberto Morelli⁴

¹National Institute of Nuclear Physics, Bologna, Italy. luca.clissa2@unibo.it.

Scientific Data

|February 10, 2024

Summary

This summary is machine-generated.

This study introduces Fluorescent Neuronal Cells v2, a dataset of 1874 images and 750 annotations for deep learning in life sciences. It aids research in semantic segmentation, object detection, and counting of neuronal cells.

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Last Updated: Jul 3, 2025

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A Rapid Approach to High-Resolution Fluorescence Imaging in Semi-Thick Brain Slices

A Rapid Approach to High-Resolution Fluorescence Imaging in Semi-Thick Brain Slices

Published on: July 26, 2011

Area of Science:

Life Sciences
Deep Learning
Computer Vision
Neuroscience

Background:

Fluorescence microscopy is crucial for visualizing cellular structures.
Analyzing complex microscopy data requires advanced computational tools.
Existing datasets may lack the diversity and annotations needed for robust model training.

Purpose of the Study:

To introduce Fluorescent Neuronal Cells v2, a comprehensive dataset for life sciences and deep learning research.
To provide high-resolution images with detailed ground-truth annotations for various computer vision tasks.
To facilitate methodological advancements and benchmarking in image analysis.

Main Methods:

Compilation of 1874 high-resolution fluorescence microscopy images of rodent neuronal cells.
Inclusion of 750 ground-truth annotations for semantic segmentation, object detection, and cell counting.
Staining of neuronal cell nuclei and cytoplasm with diverse markers.

Main Results:

A rich dataset enabling diverse machine learning applications.
Facilitation of research in unsupervised, self-supervised, and transfer learning.
Support for advancements in segmentation and detection algorithms.

Conclusions:

Fluorescent Neuronal Cells v2 is a valuable resource for advancing computer vision in life sciences.
The dataset will accelerate breakthroughs in fluorescence microscopy analysis and neuroscience.
It promotes innovative research through accessible data and comprehensive annotations.