Johannes Stegmaier

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

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.

Fast Segmentation of Stained Nuclei in Terabyte-Scale, Time Resolved 3D Microscopy Image Stacks

Automated analysis of multi-dimensional microscopy images has become an integral part of modern research in life science. Most available algorithms that provide sufficient segmentation quality, however, are infeasible for a large amount of data due to their high complexity. In this contribution we present a fast parallelized segmentation method that is especially suited for the extraction of stained nuclei from microscopy images, e.g., of developing zebrafish embryos. The idea is to transform the input image based on gradient and normal directions in the proximity of detected seed points such that it can be handled by straightforward global thresholding like Otsu’s method. We evaluate the quality of the obtained segmentation results on a set of real and simulated benchmark images in 2D and 3D and show the algorithm’s superior performance compared to other state-of-the-art algorithms. We achieve an up to ten-fold decrease in processing times, allowing us to process large data sets while still providing reasonable segmentation results.

An objective comparison of cell-tracking algorithms

We present a combined report on the results of three editions of the Cell Tracking Challenge, an ongoing initiative aimed at promoting the development and objective evaluation of cell segmentation and tracking algorithms. With 21 participating algorithms and a data repository consisting of 13 data sets from various microscopy modalities, the challenge displays todays state-of-the-art methodology in the field. We analyzed the challenge results using performance measures for segmentation and tracking that rank all participating methods. We also analyzed the performance of all of the algorithms in terms of biological measures and practical usability. Although some methods scored high in all technical aspects, none obtained fully correct solutions. We found that methods that either take prior information into account using learning strategies or analyze cells in a global spatiotemporal video context performed better than other methods under the segmentation and tracking scenarios included in the challenge.

An ensemble-averaged, cell density-based digital model of zebrafish embryo development derived from light-sheet microscopy data with single-cell resolution

A new era in developmental biology has been ushered in by recent advances in the quantitative imaging of all-cell morphogenesis in living organisms. Here we have developed a light-sheet fluorescence microscopy-based framework with single-cell resolution for identification and characterization of subtle phenotypical changes of millimeter-sized organisms. Such a comparative study requires analyses of entire ensembles to be able to distinguish sample-to-sample variations from definitive phenotypical changes. We present a kinetic digital model of zebrafish embryos up to 16 h of development. The model is based on the precise overlay and averaging of data taken on multiple individuals and describes the cell density and its migration direction at every point in time. Quantitative metrics for multi-sample comparative studies have been introduced to analyze developmental variations within the ensemble. The digital model may serve as a canvas on which the behavior of cellular subpopulations can be studied. As an example, we have investigated cellular rearrangements during germ layer formation at the onset of gastrulation. A comparison of the one-eyed pinhead (oep) mutant with the digital model of the wild-type embryo reveals its abnormal development at the onset of gastrulation, many hours before changes are obvious to the eye.

Robust Optimal Design of Experiments for Model Discrimination Using an Interactive Software Tool

Mathematical modeling of biochemical processes significantly contributes to a better understanding of biological functionality and underlying dynamic mechanisms. To support time consuming and costly lab experiments, kinetic reaction equations can be formulated as a set of ordinary differential equations, which in turn allows to simulate and compare hypothetical models in silico. To identify new experimental designs that are able to discriminate between investigated models, the approach used in this work solves a semi-infinite constrained nonlinear optimization problem using derivative based numerical algorithms. The method takes into account parameter variabilities such that new experimental designs are robust against parameter changes while maintaining the optimal potential to discriminate between hypothetical models. In this contribution we present a newly developed software tool that offers a convenient graphical user interface for model discrimination. We demonstrate the beneficial operation of the discrimination approach and the usefulness of the software tool by analyzing a realistic benchmark experiment from literature. New robust optimal designs that allow to discriminate between the investigated model hypotheses of the benchmark experiment are successfully calculated and yield promising results. The involved robustification approach provides maximally discriminating experiments for the worst parameter configurations, which can be used to estimate the meaningfulness of upcoming experiments. A major benefit of the graphical user interface is the ability to interactively investigate the model behavior and the clear arrangement of numerous variables. In addition to a brief theoretical overview of the discrimination method and the functionality of the software tool, the importance of robustness of experimental designs against parameter variability is demonstrated on a biochemical benchmark problem. The software is licensed under the GNU General Public License and freely available at http://sourceforge.net/projects/mdtgui/.

Information Fusion of Image Analysis, Video Object Tracking, and Data Mining of Biological Images using the Open Source MATLAB Toolbox Gait-CAD

Automated imaging has become a commonplace and widespread technique for researchers aiming to increase both biological and medical knowledge. Systematic high-throughput screening approaches produce a vast amount of data that needs to be quantified automatically. To address this problem, we present an extended version of the open-source MATLAB toolbox Gait-CAD providing integrated tools for automated image analysis, video object tracking and data mining. Gait-CAD offers a convenient graphical user interface (GUI) and is shipped with a great selection of predefined, customizable plugins for both image analysis and data mining. The plugin-based architecture and templates for customized tools provide easy expandability in order to develop comprehensive data-analysis pipelines. Process automation via batch-files and macro recording functionality enables the handling of large datasets like multi-dimensional 2D or 3D images and videos. The scope of the presented tools ranges from automated high-throughput toxicity testing in zebrafish embryos to cellular analysis tasks in developmental biology. In both examples, the toolbox is successfully applied for pre-processing, normalization, segmentation and tracking of spatio-temporal microscopy images, as well as for subsequent data mining and report generation. As automatically acquired images tend to differ in each recording, researchers can significantly accelerate parameter adjustments, process automation and result visualization by using the presented software. The toolbox is not limited to these applications, but they already reveal the great potential of the extended Gait-CAD release. The presented toolbox is a powerful instrument for data analysis in life sciences. A user-friendly GUI provides functionality to create sophisticated approaches even for users with limited programming knowledge. Licensed under the GNU General Public License (GNU-GPL), the toolkit is freely available and can be downloaded at http://sourceforge.net/projects/gait-cad/.

Generating semi-synthetic validation benchmarks for embryomics

Systematic validation is an essential part of algorithm development. The enormous dataset sizes and the complexity observed in many recent time-resolved 3D fluorescence microscopy imaging experiments, however, prohibit a comprehensive manual ground truth generation. Moreover, existing simulated benchmarks in this field are often too simple or too specialized to sufficiently validate the observed image analysis problems. We present a new semi-synthetic approach to generate realistic 3D+t benchmarks that combines challenging cellular movement dynamics of real embryos with simulated fluorescent nuclei and artificial image distortions including various parametrizable options like cell numbers, acquisition deficiencies or multiview simulations. We successfully applied the approach to simulate the development of a zebrafish embryo with thousands of cells over 14 hours of its early existence.

Zebrafish biosensor for toxicant induced muscle hyperactivity.

Robust and sensitive detection systems are a crucial asset for risk management of chemicals, which are produced in increasing number and diversity. To establish an in vivo biosensor system with quantitative readout for potential toxicant effects on motor function, we generated a transgenic zebrafish line TgBAC(hspb11:GFP) which expresses a GFP reporter under the control of regulatory elements of the small heat shock protein hspb11. Spatiotemporal hspb11 transgene expression in the musculature and the notochord matched closely that of endogenous hspb11 expression. Exposure to substances that interfere with motor function induced a dose-dependent increase of GFP intensity beginning at sub-micromolar concentrations, while washout of the chemicals reduced the level of hspb11 transgene expression. Simultaneously, these toxicants induced muscle hyperactivity with increased calcium spike height and frequency. The hspb11 transgene up-regulation induced by either chemicals or heat shock was eliminated after co-application of the anaesthetic MS-222. TgBAC(hspb11:GFP) zebrafish embryos provide a quantitative measure of muscle hyperactivity and represent a robust whole organism system for detecting chemicals that affect motor function.

XPIWIT—an XML pipeline wrapper for the Insight Toolkit

UNLABELLED The Insight Toolkit offers plenty of features for multidimensional image analysis. Current implementations, however, often suffer either from a lack of flexibility due to hard-coded C++ pipelines for a certain task or by slow execution times, e.g. caused by inefficient implementations or multiple read/write operations for separate filter execution. We present an XML-based wrapper application for the Insight Toolkit that combines the performance of a pure C++ implementation with an easy-to-use graphical setup of dynamic image analysis pipelines. Created XML pipelines can be interpreted and executed by XPIWIT in console mode either locally or on large clusters. We successfully applied the software tool for the automated analysis of terabyte-scale, time-resolved 3D image data of zebrafish embryos. AVAILABILITY AND IMPLEMENTATION XPIWIT is implemented in C++ using the Insight Toolkit and the Qt SDK. It has been successfully compiled and tested under Windows and Unix-based systems. Software and documentation are distributed under Apache 2.0 license and are publicly available for download at https://bitbucket.org/jstegmaier/xpiwit/downloads/. CONTACT johannes.stegmaier@kit.edu SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

Automation strategies for large-scale 3D image analysis

Abstract New imaging techniques enable visualizing and analyzing a multitude of unknown phenomena in many areas of science at high spatio-temporal resolution. The rapidly growing amount of image data, however, can hardly be analyzed manually and, thus, future research has to focus on automated image analysis methods that allow one to reliably extract the desired information from large-scale multidimensional image data. Starting with infrastructural challenges, we present new software tools, validation benchmarks and processing strategies that help coping with large-scale image data. The presented methods are illustrated on typical problems observed in developmental biology that can be answered, e.g., by using time-resolved 3D microscopy images.