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Related Experiment Video

Updated: May 17, 2025

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

Scale selection and machine learning based cell segmentation and tracking in time lapse microscopy.

Nagasoujanya Annasamudram¹, Jian Zhao¹, Olaitan Oluwadare¹

¹Division of Physics, Engineering, Mathematics and Computer Science, Delaware State University, Dover, 19901, DE, USA.

Scientific Reports

|April 5, 2025

Summary

This summary is machine-generated.

This study presents a new automated method for cell segmentation and tracking using multi-scale interest points and neural networks. The approach offers competitive performance and generalizes well across diverse cell types and imaging techniques.

Keywords:

Automated scale selection Cell tracking Neural net Spatio-temporal features Time-lapse series

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Area of Science:

Biomedical Engineering
Computational Biology
Microscopy Image Analysis

Background:

Manual cell tracking is time-consuming and lacks reproducibility.
Automated cell tracking faces challenges with intensity variations and resolution limits.

Purpose of the Study:

To develop a comprehensive methodology for automated cell segmentation and tracking.
To improve the accuracy and generalizability of cell motion analysis.

Main Methods:

Utilized multi-scale space-time interest point detection for automatic scale selection and segmentation.
Employed a neural network with class prototype balancing for cell region detection.
Applied a graph-based framework for track generation and cell event detection.

Main Results:

The proposed method demonstrated competitive performance against top techniques in the Cell Tracking Challenge (CTC).
Achieved excellent generalization across diverse cell types, sizes, and imaging modalities.
Validated through rigorous evaluation on time-lapse microscopy sequences.

Conclusions:

The developed methodology offers an effective and robust solution for automated cell segmentation and tracking.
Provides a valuable tool for disease mechanism research and treatment evaluation.
Publicly available code facilitates wider adoption and further development.