title: Enhancing automated micrograph-based evaluation of LPS-stimulated macrophage spreading.
abstract: To evaluate macrophage spreading in immunofluorescence images of macrophages for surface protein CD11b and nuclear counterstaining with DAPI, it is necessary to measure the size of the macrophages at different time points after stimulation. Manual evaluation of fluorescent micrographs is usually a time-consuming and error-prone task, with poor reproducibility. Automatic image analysis methods can be used to improve the results. The quality of the analysis with these methods mainly depends on the quality of the image segmentation. A segmentation and quantification scheme based on shading correction, k-means clustering, and fast marching level sets has been developed for the purpose. An initial application of this approach showed that separating touching and overlapping cells in particular suffers severely in the inevitably blurred conditions, leading to partly erroneous measurements of macrophage spreading. An alternative method of segmentation in fluorescent micrographs was therefore investigated and evaluated in this study. The proposed approach uses a methodology that separates foreground objects from background objects on the basis of Boykov's graph cuts. In this process, a rough estimation of background pixels is used for background seeds. To identify foreground seeds, a difference of Gaussian band pass filter based workflow is developed. Information on foreground and background seeds is then used for a gradient magnitude based graph cut resulting in a robust figure-ground separation method. In addition, a fast marching level set approach is used in the post-processing step, which makes it possible to split touching cells by incorporating information about the cell nuclei. An evaluation based on a total of 553 manually labeled macrophages depicted in 21 micrographs showed that the proposed method significantly improves segmentation and splitting performance for fluorescent micrographs of LPS-stimulated macrophages and reduces the rate of error in automated analysis of macrophage spreading in comparison with alternative methods.
authors: Department of Image Processing and Biomedical Engineering, Fraunhofer Institute for Integrated Circuits (IIS), Erlangen, Germany. christian.held@iis.fraunhofer.de
authors: Christian
authors: C
authors: Held
authors: Jens
authors: J
authors: Wenzel
authors: Veit
authors: V
authors: Wiesmann
authors: Ralf
authors: R
authors: Palmisano
authors: Roland
authors: R
authors: Lang
authors: Thomas
authors: T
authors: Wittenberg
mesh_terms: D000465:Algorithms; D000818:Animals; D015415:Biomarkers / Q000032:analysis; D001854:Bone Marrow Cells / Q000187:drug effects* / Q000378:metabolism / Q000648:ultrastructure; D039481:CD11b Antigen / Q000032:analysis; D002467:Cell Nucleus / Q000187:drug effects* / Q000648:ultrastructure; D048429:Cell Size; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007211:Indoles / Q000032:analysis; D008070:Lipopolysaccharides / Q000494:pharmacology*; D008262:Macrophage Activation / Q000187:drug effects; D008264:Macrophages / Q000187:drug effects* / Q000378:metabolism / Q000648:ultrastructure; D051379:Mice; D008856:Microscopy, Fluorescence; D010363:Pattern Recognition, Automated; D015203:Reproducibility of Results; D059629:Signal-To-Noise Ratio; D059010:Single-Cell Analysis / Q000379:methods
chemical_list: D015415:Biomarkers; D039481:CD11b Antigen; D007211:Indoles; D008070:Lipopolysaccharides; C007293:DAPI