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Related Experiment Video

Updated: Sep 13, 2025

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

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Published on: October 27, 2023

Improving Cell Detection and Tracking in Microscopy Images Using YOLO and an Enhanced DeepSORT Algorithm.

Mokhaled N A Al-Hamadani^1,2,3, Richard Poroszlay¹, Gabor Szeman-Nagy⁴

¹Department of Data Science and Visualization, Faculty of Informatics, University of Debrecen, 4032 Debrecen, Hungary.

Sensors (Basel, Switzerland)

|July 30, 2025

Summary

This summary is machine-generated.

This study introduces an automated pipeline for cell detection and tracking in microscopy images. Combining YOLOv8x and DeepSORT significantly improves cell tracking accuracy and data completeness for biological research.

Keywords:

DeepSORT algorithm UKF YOLO model cell detection cell tracking microscopy images multi-scale ResNet50

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Single-Molecule Tracking Microscopy - A Tool for Determining the Diffusive States of Cytosolic Molecules

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Published on: September 5, 2019

Lens-free Video Microscopy for the Dynamic and Quantitative Analysis of Adherent Cell Culture

Lens-free Video Microscopy for the Dynamic and Quantitative Analysis of Adherent Cell Culture

Published on: February 23, 2018

Area of Science:

Biotechnology
Biomedical Research
Cellular Biology

Background:

Accurate cell detection and tracking in microscopy images are vital for biological research.
Existing methods face challenges in maintaining data continuity and preventing cell identity switches.

Purpose of the Study:

To develop an integrated pipeline for automated cell detection and tracking in microscopy image series.
To enhance the accuracy and completeness of cell tracking data by combining deep learning with robust tracking algorithms.

Main Methods:

Utilized a fine-tuned YOLOv8x model for initial cell and cell division detection.
Integrated the DeepSORT tracking algorithm to improve data association and reduce cell identity switches.
Employed a pre-trained convolutional network within DeepSORT for robust multi-object tracking.

Main Results:

The integrated pipeline achieved a recall of 93.21% using the enhanced DeepSORT algorithm.
The original YOLOv8x model achieved a recall of 53.47%, highlighting the improvement from DeepSORT.
The system demonstrated effective extraction of detailed cellular process information from image datasets.

Conclusions:

The proposed pipeline offers a reliable method for approximating cellular processes in culture environments.
The combination of YOLOv8x and DeepSORT significantly enhances cell tracking performance and data integrity.
This approach addresses key challenges in automated cell analysis for biotechnology and biomedical applications.