title: Quantitative analysis of the heterogeneous population of endocytic vesicles.
abstract: The quantitative characterization of endocytic vesicles in images acquired with microscope is critically important for deciphering of endocytosis mechanisms. Image segmentation is the most important step of quantitative image analysis. In spite of availability of many segmentation methods, the accurate segmentation is challenging when the images are heterogeneous with respect to object shapes and signal intensities what is typical for images of endocytic vesicles. We present a Morphological reconstruction and Contrast mapping segmentation method (MrComas) for the segmentation of the endocytic vesicle population that copes with the heterogeneity in their shape and intensity. The method uses morphological opening and closing by reconstruction in the vicinity of local minima and maxima respectively thus creating the strong contrast between their basins of attraction. As a consequence, the intensity is flattened within the objects and their edges are enhanced. The method accurately recovered quantitative characteristics of synthetic images that preserve characteristic features of the endocytic vesicle population. In benchmarks and quantitative comparisons with two other popular segmentation methods, namely manual thresholding and Squash plugin, MrComas shows the best segmentation results on real biological images of EGFR (Epidermal Growth Factor Receptor) endocytosis. As a proof of feasibility, the method was applied to quantify the dynamical behavior of Early Endosomal Autoantigen 1 (EEA1)-positive endosome subpopulations during EGF-stimulated endocytosis.
authors.affiliation: * Mathematical Biology and Bioinformatics Lab, Peter the Great St. Petersburg Polytechnic University, 29, Polytechnicheskaya, St. Petersburg, 195251, Russia.
authors.forename: Konstantin
authors.identifier: http://orcid.org/0000-0001-6765-236X
authors.initials: K
authors.lastname: Kozlov
authors.affiliation: † Institute of Cytology, RAS, 4, Tikhoretsky ave, St. Petersburg, 194164, Russia.
authors.forename: Vera
authors.initials: V
authors.lastname: Kosheverova
authors.affiliation: † Institute of Cytology, RAS, 4, Tikhoretsky ave, St. Petersburg, 194164, Russia.
authors.forename: Rimma
authors.initials: R
authors.lastname: Kamentseva
authors.affiliation: † Institute of Cytology, RAS, 4, Tikhoretsky ave, St. Petersburg, 194164, Russia.
authors.forename: Marianna
authors.initials: M
authors.lastname: Kharchenko
authors.affiliation: * Mathematical Biology and Bioinformatics Lab, Peter the Great St. Petersburg Polytechnic University, 29, Polytechnicheskaya, St. Petersburg, 195251, Russia.
authors.forename: Alena
authors.initials: A
authors.lastname: Sokolkova
authors.affiliation: † Institute of Cytology, RAS, 4, Tikhoretsky ave, St. Petersburg, 194164, Russia.|‡ St. Petersburg State University, 7-9, Universitetskaya emb, St. Petersburg, 199034, Russia.
authors.forename: Elena
authors.initials: E
authors.lastname: Kornilova
authors.affiliation: * Mathematical Biology and Bioinformatics Lab, Peter the Great St. Petersburg Polytechnic University, 29, Polytechnicheskaya, St. Petersburg, 195251, Russia.
authors.forename: Maria
authors.initials: M
authors.lastname: Samsonova
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D004705:Endocytosis / Q000502:physiology; D011992:Endosomes / Q000378:metabolism; D066246:ErbB Receptors / Q000378:metabolism; D006367:HeLa Cells; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D022161:Transport Vesicles; D033921:Vesicular Transport Proteins / Q000378:metabolism
chemical_list: D033921:Vesicular Transport Proteins; C093846:early endosome antigen 1; C512478:EGFR protein, human; D066246:ErbB Receptors
keywords: Image processing algorithm; colocalization; endocytosis; image segmentation