19 February 2013 Graph cut and image intensity-based splitting improves nuclei segmentation in high-content screening
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Quantification of phenotypes in high-content screening experiments depends on the accuracy of single cell analysis. In such analysis workflows, cell nuclei segmentation is typically the first step and is followed by cell body segmentation, feature extraction, and subsequent data analysis workflows. Therefore, it is of utmost importance that the first steps of high-content analysis are done accurately in order to guarantee correctness of the final analysis results. In this paper, we present a novel cell nuclei image segmentation framework which exploits robustness of graph cut to obtain initial segmentation for image intensity-based clump splitting method to deliver the accurate overall segmentation. By using quantitative benchmarks and qualitative comparison with real images from high-content screening experiments with complicated multinucleate cells, we show that our method outperforms other state-of-the-art nuclei segmentation methods. Moreover, we provide a modular and easy-to-use implementation of the method for a widely used platform.
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Muhammad Farhan, Muhammad Farhan, Pekka Ruusuvuori, Pekka Ruusuvuori, Mario Emmenlauer, Mario Emmenlauer, Pauli Rämö, Pauli Rämö, Olli Yli-Harja, Olli Yli-Harja, Christoph Dehio, Christoph Dehio, "Graph cut and image intensity-based splitting improves nuclei segmentation in high-content screening", Proc. SPIE 8655, Image Processing: Algorithms and Systems XI, 86550F (19 February 2013); doi: 10.1117/12.2003243; https://doi.org/10.1117/12.2003243

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