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Related Concept Videos

Super-resolution Fluorescence Microscopy01:37

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 developed.

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Related Experiment Video

Updated: Jun 26, 2026

Simultaneous Multicolor Imaging of Biological Structures with Fluorescence Photoactivation Localization Microscopy
12:51

Simultaneous Multicolor Imaging of Biological Structures with Fluorescence Photoactivation Localization Microscopy

Published on: December 9, 2013

Adaptive Multi-Scale Fusion Enhanced RT-DETR for Efficient Cyanobacteria Detection in Microscopic Images.

Jianxing Li1,2, Shizhi Zheng1,2, Yu Chen3

  • 1School of Electrical and Information Engineering, Fujian University of Technology, Fuzhou 350118, China.

Biology
|June 25, 2026
PubMed
Summary

This study introduces an improved RT-DETR model for accurate cyanobacteria detection in microscopic images, enhancing automated water quality monitoring. The new framework balances high detection accuracy with computational efficiency for practical applications.

Keywords:
RT-DETRWise-IoUcyanobacteria detectionmicroscopic image analysismulti-scale feature fusionobject detection

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Early Detection of Cyanobacterial Blooms and Associated Cyanotoxins using Fast Detection Strategy
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Related Experiment Videos

Last Updated: Jun 26, 2026

Simultaneous Multicolor Imaging of Biological Structures with Fluorescence Photoactivation Localization Microscopy
12:51

Simultaneous Multicolor Imaging of Biological Structures with Fluorescence Photoactivation Localization Microscopy

Published on: December 9, 2013

Early Detection of Cyanobacterial Blooms and Associated Cyanotoxins using Fast Detection Strategy
07:13

Early Detection of Cyanobacterial Blooms and Associated Cyanotoxins using Fast Detection Strategy

Published on: February 25, 2021

Area of Science:

  • Environmental Science
  • Computer Vision
  • Microbiology

Background:

  • Automated water quality monitoring relies on accurate cyanobacteria detection in microscopic images.
  • Challenges include complex backgrounds, scale variation, and sample quality issues.

Purpose of the Study:

  • To develop an adaptive multi-scale fusion enhanced RT-DETR framework for improved cyanobacteria detection.
  • To enhance detection accuracy and computational efficiency in microscopic imaging.

Main Methods:

  • An adaptive multi-scale fusion enhanced RT-DETR framework was proposed.
  • Improvements included SeFaster module, a high-level screening-feature fusion pyramid network, and Wise-IoU loss.
  • The model was tested on the reorganized EMDS-7 dataset.

Main Results:

  • The proposed method achieved 79.05% mAP@0.5 and 66.03% mAP@0.5:0.95.
  • It demonstrated the highest mAP@0.5 across seven cyanobacteria categories.
  • The model showed stability and transferability in cross-dataset evaluations.

Conclusions:

  • The developed framework offers effective cyanobacteria detection in microscopic images.
  • It presents a favorable balance between detection accuracy and computational efficiency.
  • The approach shows potential for real-world automated water quality monitoring.