Real-Time Three-Dimensional Cell Segmentation in Large-Scale Microscopy Data of Developing Embryos

Abstract

We present the Real-time Accurate Cell-shape Extractor (RACE), a high-throughput image analysis framework for automated three-dimensional cell segmentation in large-scale images. RACE is 55-330 times faster and 2-5 times more accurate than state-of-the-art methods. We demonstrate the generality of RACE by extracting cell-shape information from entire Drosophila, zebrafish, and mouse embryos imaged with confocal and light-sheet microscopes. Using RACE, we automatically reconstructed cellular-resolution tissue anisotropy maps across developing Drosophila embryos and quantified differences in cell-shape dynamics in wild-type and mutant embryos. We furthermore integrated RACE with our framework for automated cell lineaging and performed joint segmentation and cell tracking in entire Drosophila embryos. RACE processed these terabyte-sized datasets on a single computer within 1.4 days. RACE is easy to use, as it requires adjustment of only three parameters, takes full advantage of state-of-the-art multi-core processors and graphics cards, and is available as open-source software for Windows, Linux, and Mac OS.

SEGGA: a toolset for rapid automated analysis of epithelial cell polarity and dynamics

“…However, in the initial SEGGA output, 3-5% of cells could not be associated with a corresponding cell at the previous or following time points, an error rate comparable to the reported accuracy of other methods (Mosaliganti et al, 2012;Stegmaier et al, 2016). This frequency of errors is not compatible with long-term tracking, as many cell trajectories are interrupted by segmentation errors during the course of a typical movie.…”

“…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 Aigouy et 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.…”

“…Although a tremendous amount of information about singlecell and collective cell behaviors can be obtained from live imaging studies, the availability of accurate and high-throughput methods for cell tracking and analysis is currently a rate-limiting step in making use of this information. Image analysis tools that detect cell nuclei can reliably track nuclear movements in C. elegans (Bao et al, 2006;Santella et al, 2010;Giurumescu et al, 2012), zebrafish (Keller et al, 2008), Drosophila (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 Blankenship et al, 2006;Williams et al, 2014).…”

Multiplex, quantitative cellular analysis in large tissue volumes with clearing-enhanced 3D microscopy (C e 3D)

“…Although successful cellular segmentation was achieved with the C e 3D histo-cytometry pipeline, limitations in parallel processing in the segmentation software available to us necessitated processing of very large image datasets as separate fragments. Such limitations are likely to be temporary, as substantial interest in large 4D datasets obtained with light-sheet microscopy has promoted development of novel open-source solutions for data handling and analysis of extremely large datasets (31,32). Finally, commercially available fluorophores with distinctly separable excitation or emission spectra currently limit the number of probes to a maximum of 8-15.…”

Seeing is believing: multi-scale spatio-temporal imaging towards in vivo cell biology

“…In addition, several groups have implemented SPIM to accelerate commonly performed imaging. For example, MuVi-SPIM together with a customized software package can be used to increase acquisition speed and analysis by several orders of magnitude, which allows to -almost instantaneouslyrecord individual cell shapes over an entire embryo (Stegmaier et al, 2016).…”