Three‐dimensional segmentation of nuclei and mitotic chromosomes for the study of cell divisions in live Drosophila embryos

Abstract

Drosophila embryogenesis is an established model to investigate mechanisms and genes related to cell divisions in an intact multicellular organism. Progression through the cell cycle phases can be monitored in vivo using fluorescently labeled fusion proteins and time-lapse microscopy. To measure cellular properties in microscopic images, accurate and fast image segmentation methods are a critical prerequisite. To quantify static and dynamic features of interphase nuclei and mitotic chromosomes, we developed a three-dimensional (3D) segmentation method based on multiple level sets. We tested our method on 3D time-series images of live embryos expressing histone-2Av-green fluorescence protein. Our method is robust to low signal-to-noise ratios inherent to high-speed imaging, fluorescent signals in the cytoplasm, and dynamic changes of shape and texture. Comparisons with manual ground-truth segmentations showed that our method achieves more than 90% accuracy on the object as well as voxel levels and performs consistently throughout all cell cycle phases and developmental stages from syncytial blastoderm to postblastoderm mitotic domains. ' 2011 International Society for Advancement of Cytometry

Current automated 3D cell detection methods are not a suitable replacement for manual stereologic cell counting

“…The 3D MLS algorithm is one of several cell segmentation techniques based on level-set methods, which model the boundary of an object as the 0-level of an equation (Dzyubachyk et al, 2008; Chinta and Wasser, 2012; Qi et al, 2012). The shape of the boundary is controlled by modifying the equation based on image content and model constraints.…”

“…Default operation involved selecting Tools/3D Cell Nuclei Segmentation and accepting all default parameters. Manual operation involved selecting algorithm parameters according to instructions provided in the online supplemental material for a study by Chinta and Wasser (2012). Several iterations of empirical parameter tuning were completed until the segmentation result appeared to capture the greatest number of cells with the least amount of oversegmentation and undersegmentation.…”

“…Numerous methods for automated cell detection in microscopy images have been proposed in the literature over the past 50 years (reviewed in Meijering, 2012). We performed an analysis of the theory behind many of these algorithms and found that the following methods are generally compatible with automated 3D cell detection in stereologic cell counting: (i) the technology implemented in the FARSIGHT toolkit for automated 3D cell detection (henceforth referred to as “FARSIGHT”) (Lin et al, 2007; Bjornsson et al, 2008; Al-Kofahi et al, 2010); (ii) the 3D multiple level sets (3D MLS) segmentation method (Chinta and Wasser, 2012); and (iii) the 3D Object Counter plug-in for ImageJ (Bolte and Cordelières, 2006). Additional information about the theory of cell detection in general and these methods in particular appears in the Materials and Methods section below.…”

Visualização Tridimensional Do Deslocamento De Óleo Por Suspensões De Microcápsulas Flexíveis Em Meios Porosos

“…Métodos de segmentação baseados no thresholding global são malsucedidos para esses tipos de imagens que geralmente apresentam uma baixa razão de sinal para ruído e baixo contraste no primeiro plano e no plano de fundo (Chinta & Wasser, 2012).…”

“…Portanto, esforços consideráveis têm sido feitos nos últimos anos com intuito de se desenvolver softwares automatizados para segmentação 3D de objetos (Chinta & Wasser, 2012;Rajasekaran et al, 2016;Toyoshima et al, 2016).…”