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Scale Selection and Machine Learning-based Cell Segmentation and Tracking in Time Lapse Microscopy.

Nagasoujanya Annasamudram¹, Jian Zhao¹, Aashish Prashanth¹

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

Research Square

|November 22, 2024

Summary

This summary is machine-generated.

This study introduces an automated method for cell segmentation and tracking using multi-scale interest points and neural networks. The approach offers competitive performance and generalizes well across various 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 imaging
Computational biology
Cellular dynamics

Background:

Accurate cell motion monitoring is crucial for understanding diseases and treatment efficacy.
Manual cell tracking is time-consuming and lacks reproducibility.
Automated tracking faces challenges like intensity variations and resolution limits.

Purpose of the Study:

To develop a comprehensive automated methodology for cell segmentation and tracking.
To address limitations of existing automated cell tracking techniques.

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.
Developed 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 techniques.
Validated performance on time-lapse microscopy sequences.

Conclusions:

The developed methodology provides an effective and robust solution for automated cell segmentation and tracking.
This approach advances the field of quantitative cell motion analysis for biological research.