LCT-Live Cell Track®

An Automatic Cell Tracking System for the Analysis of Cell Motions.

Live-Cell Assays are used to study the dynamic functional cellular processes in High-Content Screening in many areas of live science e.g. of drug discovery processes, medical science, biological questions and other. The large amount of image data created during the screening requires automatic image-analysis procedures that can describe these dynamic processes. One class of tasks in this application is the tracking of cells and the description of the events and the changes in the cell characteristics, so that the desired information can be extracted from it based on data-mining and knowledge-discovery methods. The large amount of data requires fast processing algorithms and compact object and event descriptions that are powerful and flexible enough to describe the different events and object characteristics.

Screenshot LCT-Live Cell Track

Photo: Screenshot LCT-Live Cell Track

What is the Innovation of LCT?

We present a system that can automatically detect single cells in an image series, find the same cell in the next image and calculate the covered distances. It creates a statistic (trajectory) for each cell over the live cycle from appearing to disappearing or dividing.

Results LCT-Live Cell Track

What is the Benefit from LCT?

LCT removes you from the enormous expenditure to figure out by spot tests the behavior of the cell culture. This automat solution delivers comparable results and reduces the amount of space to store all the image information down to less then 1/100.

Features of LCT

  • Image to image cell detection and selection
  • Image to image cell movement statistic
  • Cell movement statistic over live cycle
  • Public result file format data set for use in other tools

System Requirements

  • Windows 32-bit version all up to Windows 7
  • PC with high performance (CPU speed, RAM speed, FSB speed)
  • Fast hard disk with at least three time space of the value from image sequence


  • Petra Perner, Tracking Living Cells in Microscopic Images and Description of the Kinetics of the Cells, Eds: Liya Ding, Charles Pang, Leong Mun Kew, Lakhmi C. Jain and Robert J. Howlett, Knowledge-Based and Intelligent Information & Engineering Systems 19th Annual Conference, KES-2015, Singapore, September 2015 Proceedings, Procedia Computer Science Volume 60, p. 352-361 (2015)
  • Petra Perner. Automatic Cell Tracking and Kinetic Feature Description of Cell Paths for Image Mining, In: P. Perner (Eds.), Machine Learning and Data Mining in Pattern Recognition, lnai 9166, Springer Verlag, 2015