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
-
Detection
-
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
- 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)
- Cell population tracking and lineage construction with spatiotemporal context (Kang Li, Eric D. Miller, Mei Chen)
- Filtr Kalmana - zastosowania w prostych układach sensorycznych (Jan Kędzierski)
- http://rsbweb.nih.gov/ij/docs/guide/
- http://www.imagescience.org/meijering/software/mtrackj/