Biomedical Image Processing

Cell Tracking 101

Author

inż. Krzysztof Wolski

 

Python, JS, C# programmer

Field of interests:

• Applied Computer Engineering in Medicine
• Human - computer interface design
• Gamedev
• Webdev

 

Agenda

• Introduction
• Common problems
• Manual tracking
• Automated systems
• Summary

Real life applications

• Learning about cell behavior
• New drugs research 
• Population tracking
• Environment impact 
• Infertility therapy

Difficulties

Big data

Field of view

Cell behavior 

1. Detection

2. Tracking

By detection

• Easy implementation
• Low cost

Methodology

By model evolution

• Advanced models
• Better results
• Fast growing cost 

Methodology

Counting Cells with ImageJ [4]

Tracking with MtrackJ [5]

Advanced systems

Year 2002 [1]

• Tracking E. histolytica
• Using edge with pseudopod
• Repulsive forces
• Time adapting  

Pseudopod

Edge map

Repulsive forces and topological operators

Contour change in time

Performance

44.7% -> 66.8% -> 95.2%

Year 2008 [2] 

• Complex, probabilistic model
• Multiple path filters
• Biological behavior tracking
• Movement prediction
• Multiple integrated modules

Detection

Tracking

Tracking and lineage tree

Summary

Remember: GIGO - use filters!

Make predictions 

Free software:

• OpenCv
• ImageJ/Fiji + plugins
• CellCognition

Sources

1. Segmentation and Tracking of Migrating Cells in Videomicroscopy With Parametric Active Contours: A Tool for Cell-Based Drug Testing (Christophe Zimmer*, Elisabeth Labruyère, Vannary Meas-Yedid)
2. Cell population tracking and lineage construction with spatiotemporal context (Kang Li, Eric D. Miller, Mei Chen)
3. Filtr Kalmana - zastosowania w prostych układach sensorycznych (Jan Kędzierski)
4. http://rsbweb.nih.gov/ij/docs/guide/
5. http://www.imagescience.org/meijering/software/mtrackj/

Thank you for your attention