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Generator Platform of Benchmark Time-Lapsed Images Development of Cell Tracking Algorithms: Implementation of New Features Towards a Realistic Simulation of the Cell Spatial and Temporal Organization

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Simulation and Modeling Methodologies, Technologies and Applications (SIMULTECH 2016)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 676))

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Abstract

Recent developments in live-cell microscopy imaging have led to the emergence of Single Cell Biology. This field has also been supported by the development of cell segmentation and tracking algorithms for data extraction. The validation of these algorithms requires benchmark databases, with manually labeled or artificially generated images, so that the ground truth is known. To generate realistic artificial images, we have developed a simulation platform capable of generating biologically inspired objects with various shapes and size, which are able to grow, divide, move and form specific clusters. Using this platform, we compared four tracking algorithms: Simple Nearest-Neighbor (NN), NN with Morphology (NNm) and two DBSCAN-based methodologies. We show that Simple NN performs well on objects with small velocities, while the others perform better for higher velocities and when objects form clusters. This platform for benchmark images generation and image analysis algorithms testing is openly available at (http://griduni.uninova.pt/Clustergen/ClusterGen_v1.0.zip).

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Notes

  1. 1.

    Tool available at: http://griduni.uninova.pt/Clustergen/ ClusterGen_v1.0.zip.

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Acknowledgments

Work supported by the Portuguese Foundation for Science and Technology (FCT/MCTES) through a PhD Scholarship, ref. SFRH/BD/88987/2012 to LM, SADAC project (ref. PTDC/BBB-MET/1084/2012) and by FCT Strategic Program UID/EEA/00066/203 of UNINOVA, CTS. This work is also funded by the Academy of Finland [refs. 295027 and 305342 to ASR] and the Jane and Aatos Erkko Foundation [ref. 5-3416-12 to ASR].

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Authors and Affiliations

  1. Computational Intelligence Group of CTS/UNINOVA, Faculdade de Ciências e Tecnologia da, Universidade Nova de Lisboa, Quinta da Torre, 2829-516, Caparica, Portugal

    Leonardo Martins, Pedro Canelas, André Mora & José Fonseca

  2. Laboratory of Biosystem Dynamics, Department of Signal Processing, Tampere University of Technology, Tampere, Finland

    Andre S. Ribeiro

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  1. Leonardo Martins
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  4. Andre S. Ribeiro
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Correspondence to Leonardo Martins .

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Editors and Affiliations

  1. King Abdullah II School of Information Technology, The University of Jordan, Amman, Jordan

    Mohammad S. Obaidat

  2. School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, Ontario, Canada

    Tuncer Ören

  3. Department of Modelling and Simulation, Riga Technical University, Riga, Latvia

    Yuri Merkuryev

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Martins, L., Canelas, P., Mora, A., Ribeiro, A.S., Fonseca, J. (2018). Generator Platform of Benchmark Time-Lapsed Images Development of Cell Tracking Algorithms: Implementation of New Features Towards a Realistic Simulation of the Cell Spatial and Temporal Organization. In: Obaidat, M., Ören, T., Merkuryev, Y. (eds) Simulation and Modeling Methodologies, Technologies and Applications. SIMULTECH 2016. Advances in Intelligent Systems and Computing, vol 676. Springer, Cham. https://doi.org/10.1007/978-3-319-69832-8_4

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