19 February 2013 Graph cut and image intensity-based splitting improves nuclei segmentation in high-content screening
Author Affiliations +
Abstract
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.
© (2013) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Muhammad Farhan, Pekka Ruusuvuori, Mario Emmenlauer, Pauli Rämö, Olli Yli-Harja, 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
PROCEEDINGS
10 PAGES


SHARE
Back to Top