(B) Semi-automated error correction tools improve the accuracy of segmentation

(B) Semi-automated error correction tools improve the accuracy of segmentation. pattern that is dynamically remodeled in response to changes in cell orientation. These findings reveal an unexpected plasticity that maintains coordinated planar polarity in actively (+)-ITD 1 moving populations through the continual realignment of cell polarity with the tissue axes. (Bao et al., 2006; Santella et al., 2010; Giurumescu et al., 2012), zebrafish (Keller et al., 2008), (McMahon et al., 2008; Schindelin et al., 2012; Stegmaier et al., 2016) and mice (Lou et al., 2014). However, it is not possible to accurately determine cell shape and interactions from the positions of cell nuclei, as mathematical approaches that predict the outer contours of cells based on the locations of the cell centers often fail for cells that are elongated or irregular in shape, which are typical of developing epithelia (Zallen and Zallen, 2004; Blankenship et al., 2006; Williams et al., 2014). Although computationally challenging, image analysis tools that directly detect cell boundaries are necessary to rigorously analyze cell shape, interactions and polarity in order to determine how changes in these properties contribute to tissue structure. Time-lapse movies of cell behaviors are essential to elucidating mechanisms of epithelial morphogenesis at single-cell resolution. However, long-term tracking studies of cells in tissues are currently limited by the accuracy and throughput of available image analysis methods. Fully automated methods for image segmentation and analysis, which are optimized for speed, increase the throughput of data analysis by tolerating a non-negligible frequency of errors that would otherwise require substantial effort to correct. These methods are well suited for large tissues in which error correction is impractical, short-term behaviors during which time errors are less likely to accumulate, and tissues that do not undergo substantial rearrangement (Blanchard et al., 2009; Aigouy et (+)-ITD 1 al., 2010; Fernandez et al., 2010; Bosveld et al., 2012; Mosaliganti et al., 2012; Khan et al., 2014; Guirao et al., 2015; Heller et al., 2016; Stegmaier et al., 2016). However, segmentation errors that lead to 1% untracked cells in each frame of a movie are predicted to interrupt more than half of all cell trajectories after 70 time points, making fully automated methods of limited use for long-term tracking. As an alternative strategy, (+)-ITD 1 several methods enable the user to inspect and manually correct the segmentation output (McMahon et al., 2008; Fernandez-Gonzalez and Zallen, 2011; Gelbart et al., 2012; Giurumescu et al., 2012; Mashburn et al., 2012; Barbier de Reuille et al., 2015; Cilla et al., 2015; Morales-Navarrete et al., 2015; Rozbicki et al., 2015). These methods have the potential to achieve high accuracy but require substantial effort to manually correct the segmentation at each time point, decreasing the throughput of these approaches. In addition, the practical applications of non-commercial image analysis tools are often limited by other considerations, such as the computational expertise required to install and troubleshoot published algorithms, the cost of commercial software packages required to run them, incomplete documentation of software dependencies and installation protocols, and the absence of integrated tools for data analysis. The development of software that is easy to use, produces rapid and accurate segmentation, and performs a wide range of measurements and analyses will be important to take advantage of live imaging technologies and make OCLN quantitative image analysis methods accessible to the scientific community. Here we describe SEGGA, an image analysis software for automated image ?SEGmentation, Graphical visualization and Analysis’ that can be used to systematically track changes in cell shape, behavior and polarity in epithelial tissues. SEGGA provides a suite of tools for fully automated image processing, image segmentation, cell tracking, data analysis and data visualization, as well as semi-automated error correction tools that expedite the process of obtaining accurate segmentation. SEGGA is available as a pre-compiled module that runs free of charge on Mac, Windows and Linux operating systems, and contains a graphical user interface that allows users with no prior computational expertise to perform all steps of image segmentation, correction and analysis. SEGGA is also available as open-source code (+)-ITD 1 that can be extended or modified in MATLAB (MathWorks). SEGGA is designed for the study of epithelial tissues, which determine the structure of many organs in the body and have several advantages in terms.

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