Tampere University of Technology

TUTCRIS Research Portal

An image generator platform to improve cell tracking algorithms simulation of objects of various morphologies, kinetics and clustering

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Details

Original languageEnglish
Title of host publicationSIMULTECH 2016 - Proceedings of the 6th International Conference on Simulation and Modeling Methodologies, Technologies and Applications
PublisherSCITEPRESS
Pages44-55
Number of pages12
ISBN (Electronic)9789897581991
Publication statusPublished - 2016
Publication typeA4 Article in a conference publication
EventINTERNATIONAL CONFERENCE ON SIMULATION AND MODELING METHODOLOGIES, TECHNOLOGIES AND APPLICATIONS -
Duration: 1 Jan 1900 → …

Conference

ConferenceINTERNATIONAL CONFERENCE ON SIMULATION AND MODELING METHODOLOGIES, TECHNOLOGIES AND APPLICATIONS
Period1/01/00 → …

Abstract

Several major advances in Cell and Molecular Biology have been made possible by recent advances in livecell microscopy imaging. To support these efforts, automated image analysis methods such as cell segmentation and tracking during a time-series analysis are needed. To this aim, one important step is the validation of such image processing methods. Ideally, the "ground truth" should be known, which is possible only by manually labelling images or in artificially produced images. To simulate artificial images, we have developed a platform for simulating biologically inspired objects, which generates bodies with various morphologies and kinetics and, that can aggregate to form clusters. Using this platform, we tested and compared four tracking algorithms: Simple Nearest-Neighbour (NN), NN with Morphology and two DBSCAN-based methods. We show that Simple NN works well for small object velocities, while the others perform better on higher velocities and when clustering occurs. Our new platform for generating new benchmark images to test image analysis algorithms is openly available at (http://griduni.uninova.pt/Clustergen/ClusterGen-v1.0.zip).

Keywords

  • Cell Tracking, Cluster Tracking, Microscopy, Synthetic Time-lapse Image Simulation

Publication forum classification

Field of science, Statistics Finland