Abstract
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.
Highlights
Cell segmentation is the process of separating every imaged cell from the background and from other cells
The automated analysis of single cells in huge datasets has a large potential in the screening of high-throughput microscopy-generated data
We have demonstrated the performance and versatility of CELLSEGM by application to a wide range of imaging examples related to cell lines
Summary
Cell segmentation is the process of separating every imaged cell from the background and from other cells. Automated cell segmentation is useful for the analysis of cells imaged by fluorescence microscopy, both in terms of objectivity and reduced work load. It enables the automatic quantification of cell characteristics for a large number of cells in 3D. The cell segmentation can be realized in CELLSEGM, and the scientists can design the post-processing module by themselves or in collaboration This enables flexible and targeted solutions to individual projects. In the light of the recent advancement of microscopical techniques with a broader application in both basic research and clinical diagnosis, this program can offer a significant contribution to robust data analysis/diagnosis, and thereby reduce bias introduced by manual sample evaluations This can potentially increase the comparability of pathological evaluations between clinics. We describe our cell segmentation tool CELLSEGM with examples of possible applications
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