Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

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...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Deep Learning-Based Quality Control Using Subcellular RNA Spatial Distribution Patterns for Cell Segmentation in Spatial Transcriptomics Data.

Small methods·2025

Same author

Super-Resolution Compatible DNA Labeling Technique Reveals Chromatin Mobility and Organization Changes During Differentiation.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2025

Same author

High-throughput screening of E3 ubiquitin ligases identifies TRIM48 as a novel negative regulator of RIG-I signaling.

Cellular signalling·2025

Same author

Morphology-dependent entry kinetics and spread of influenza A virus.

The EMBO journal·2025

Same author

MethylBERT enables read-level DNA methylation pattern identification and tumour deconvolution using a Transformer-based model.

Nature communications·2025

Same author

DNA choreography: correlating mobility and organization of DNA across different resolutions from loops to chromosomes.

Histochemistry and cell biology·2024

Same journal

Wavelet-inspired diffusion model with near-field constraint for real-time echocardiography dehazing.

Medical image analysis·2026

Same journal

Co-assistant networks by pathology foundation model and convolutional neural network for gigapixel whole slide image analysis.

Medical image analysis·2026

Same journal

MBAS2024: A large-scale benchmark for multi-class bi-atrial segmentation in multi-center contrast-enhanced MRIs.

Medical image analysis·2026

Same journal

Respiratory motion augmentation for personalized super-resolution (RMApSR) of 3D cine MR images in MRI-guided radiotherapy.

Medical image analysis·2026

Same journal

Biom3d, a modular framework to host and develop 3D segmentation methods.

Medical image analysis·2026

Same journal

Embracing intra-class heterogeneity for semi-supervised medical image segmentation: From diversity to precision.

Medical image analysis·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Nov 12, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

Deep Consensus Network: Aggregating predictions to improve object detection in microscopy images.

Thomas Wollmann¹, Karl Rohr¹

¹Biomedical Computer Vision Group, BioQuant, IPMB, Heidelberg University Im Neuenheimer Feld 267, Heidelberg, Germany.

Medical Image Analysis

|March 17, 2021

Summary

This summary is machine-generated.

Deep Consensus Network improves microscopy image analysis by accurately detecting small, clustered objects using deep learning. This novel approach enhances cell and particle identification in challenging imaging conditions.

Keywords:

Deep Learning Detection Microscopy Voting

More Related Videos

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Related Experiment Videos

Last Updated: Nov 12, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Area of Science:

Computer Vision
Bioimage Analysis
Machine Learning

Background:

Microscopy image analysis is crucial but challenging due to small, clustered objects and low signal-to-noise ratios.
Deep learning has advanced object detection, yet struggles with specific microscopy image complexities.
Existing methods often fail to adequately address the unique challenges of microscopy data.

Purpose of the Study:

To introduce Deep Consensus Network (DCN), a novel deep neural network for robust object detection in microscopy images.
To enhance the detection of small, clustered cells and particles, overcoming limitations of current computer vision approaches.
To provide a trainable, end-to-end network addressing class imbalance and algorithmic complexity in image analysis.

Main Methods:

Developed Deep Consensus Network (DCN) featuring a Feature Pyramid Network (FPN) based extractor and a Centroid Proposal Network (CPN).
Implemented an anchor regularization scheme favoring prior anchors and a novel Bayesian-derived loss function using Normalized Mutual Information (NMI) for class imbalance.
Introduced an optimized Non-Maximum Suppression (NMS) algorithm to reduce computational complexity.

Main Results:

DCN demonstrated competitive or superior performance on challenging datasets, including the TUPAC16 mitosis detection and Particle Tracking Challenge.
Experiments on synthetic data validated the robustness and properties of the proposed NMI loss function.
The improved NMS algorithm significantly reduced algorithmic complexity while maintaining detection accuracy.

Conclusions:

Deep Consensus Network offers a powerful and efficient solution for object detection in challenging microscopy images.
The novel loss function and NMS algorithm effectively address key issues like class imbalance and computational cost.
This work advances the state-of-the-art in bioimage analysis and deep learning-based object detection.