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Bioinformatics. 2019 Dec 20. pii: btz945. doi: 10.1093/bioinformatics/btz945. [Epub ahead of print]

LOBSTER: An environment to design bioimage analysis workflows for large and complex fluorescence microscopy data.

Author information

Institute for Research in Biomedicine - IRB Barcelona. The Barcelona Institute for Science and Technology - BIST, C. Baldiri Reixac, 10. E-08028 Barcelona, Spain.
Department of Developmental Biology, Instituto de Biología Molecular de Barcelona, CSIC, and Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), Barcelona, Spain.
Cancer Research Programme, Hospital del Mar Research Institute (IMIM), Barcelona, Spain (A.C. and E.I.R.).



Open source software such as ImageJ and CellProfiler greatly simplified the quantitative analysis of microscopy images but their applicability is limited by the size, dimensionality and complexity of the images under study. In contrast, software optimized for the needs of specific research projects can overcome these limitations, but they may be harder to find, set up and customize to different needs. Overall, the analysis of large, complex, microscopy images is hence still a critical bottleneck for many Life Scientists. We introduce LOBSTER (Little Objects Segmentation & Tracking Environment), an environment designed to help scientists design and customize image analysis workflows to accurately characterize biological objects from a broad range of fluorescence microscopy images, including large images exceeding workstation main memory. LOBSTER comes with a starting set of over 75 image analysis workflows and images stemming from state-of-the-art image-based research projects.


LOBSTER requires MATLAB (version > = 2015a), MATLAB Image processing toolbox, and MATLAB statistics & machine learning toolbox. Code source, online tutorials, video demonstrations, documentation and sample images are freely available from:


Supplementary data are available at Bioinformatics online.

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