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The application of fluorescence microscopy in cell biology often generates a huge amount of imaging data. Automated whole cell segmentation of such data enables the detection and analysis of individual cells, where a manual delineation is often time consuming, or practically not feasible. Furthermore, compared to manual analysis, automation normally has a higher degree of reproducibility.  CellSegm , the software presented in this work, is a  Matlab  based command line software toolbox providing an automated whole cell segmentation of images showing surface stained cells, acquired by fluorescence microscopy. It has options for both fully automated and semi-automated cell segmentation. Major algorithmic steps are: (i) smoothing, (ii) Hessian-based ridge enhancement, (iii) marker-controlled watershed segmentation, and (iv) feature-based classfication of cell candidates. Using a wide selection of image recordings and code snippets, we demonstrate that  CellSegm  has the ability to detect various types of surface stained cells in 3D. After detection and outlining of individual cells, the cell candidates can be subject to software based analysis, specified and programmed by the end-user, or they can be analyzed by other software tools. A segmentation of tissue samples with appropriate characteristics is also shown to be resolvable in  CellSegm . The command-line interface of  CellSegm  facilitates scripting of the separate tools, all implemented in  Matlab , offering a high degree of flexibility and tailored workflows for the end-user. The modularity and scripting capabilities of  CellSegm  enable automated workflows and quantitative analysis of microscopic data, suited for high-throughput image based screening.

CellSegm - a MATLAB toolbox for high-throughput 3D cell segmentation