SUMMARY: Many biochemical processes in living organisms take place inside compartments that can interact with each other and remodel over time. In a recent work, we have shown how the stochastic dynamics of a compartmentalized biochemical system can be effectively studied using moment equations. With this technique, the time evolution of a compartment population is summarized using a finite number of ordinary differential equations, which can be analyzed very efficiently. However, the derivation of moment equations by hand can become time-consuming for systems comprising multiple reactants and interactions. Here we present Compartor, a toolbox that automatically generates the moment equations associated with a user-defined compartmentalized system. Through the moment equation method, Compartor renders the analysis of stochastic population models accessible to a broader scientific community. AVAILABILITY AND IMPLEMENTATION: Compartor is provided as a Python package and is available at https://pypi.org/project/compartor/. Source code and usage tutorials for Compartor are available at https://github.com/zechnerlab/Compartor.

We propose a fast approximate solver for the combinatorial problem known as tracking-by-assignment, which we apply to cell tracking. The latter plays a key role in discovery in many life sciences, especially in cell and developmental biology. So far, in the most general setting this problem was addressed by off-the-shelf solvers like Gurobi, whose run time and memory requirements rapidly grow with the size of the input. In contrast, for our method this growth is nearly linear. Our contribution consists of a new (1) decomposable compact representation of the problem; (2) dual block-coordinate ascent method for optimizing the decomposition-based dual; and (3) primal heuristics that reconstructs a feasible integer solution based on the dual information. Compared to solving the problem with Gurobi, we observe an up to 60 times speed-up, while reducing the memory footprint significantly. We demonstrate the efficacy of our method on real-world tracking problems.

Scientific Visualization is generally based on very large and often high-dimensional data sets. In many cases (e.g., in the biomedical domain), high-resolution 3D data that changes over time and multiple data sets are involved. One important task in making sense of this data is finding interesting 2D views in the 3D space and additionally finding ways to handle changes over time. We explore how the combination of spatially aware mobiles and large displays can be used in this context. Specifically, we contribute both a precise selection technique for 2D cross sections and a concept for time-dependent 4D bookmarks that follow a point of interest over time. We implemented these techniques in a prototype application that visualizes biological Light Sheet Fluorescence Microscopy data and report on initial user feedback.

UNLABELLED: Selective Plane Illumination Microscopy (SPIM) allows to image developing organisms in 3D at unprecedented temporal resolution over long periods of time. The resulting massive amounts of raw image data requires extensive processing interactively via dedicated graphical user interface (GUI) applications. The consecutive processing steps can be easily automated and the individual time points can be processed independently, which lends itself to trivial parallelization on a high performance computing (HPC) cluster. Here, we introduce an automated workflow for processing large multiview, multichannel, multiillumination time-lapse SPIM data on a single workstation or in parallel on a HPC cluster. The pipeline relies on snakemake to resolve dependencies among consecutive processing steps and can be easily adapted to any cluster environment for processing SPIM data in a fraction of the time required to collect it. AVAILABILITY AND IMPLEMENTATION: The code is distributed free and open source under the MIT license http://opensource.org/licenses/MIT The source code can be downloaded from github: https://github.com/mpicbg-scicomp/snakemake-workflows Documentation can be found here: http://fiji.sc/Automated_workflow_for_parallel_Multiview_Reconstruction CONTACT: : [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Vol. 28 no. 22 2012, pages 3009–3011 doi:10.1093/bioinformatics/bts543 We regret that, due to a production error, the below references were incorrect in the original paper and should appear as below: Yoo,T.S. et al. (2002) Engineering and algorithm design for an image processing API: A technical report on ITK - the insight toolkit. In: Westwood, J. et al. (eds.) MMVR, pp. 586–592. Preibisch,S. et al. (2010) Into ImgLib—generic image processing in Java. In: Jahnen,A. and Moll,C. (eds.) ImageJ User and Developer Conference. Mondorf-les-Bains, Luxembourg. Rueden,C. et al. (2010) ImageJDev: Next generation ImageJ. In: Jahnen,A. and Moll,C. (eds.) ImageJ User and Developer Conference. Mondorf-les-Bains, Luxembourg. Matas,J. et al. (2002) Robust wide baseline stereo from maximally stable extremal regions. In: Marshall,D. and Rosin,P.L. (eds.) BMVC, Vol. 1, pp. 384–393. Nistér,D. and Stewénius,H. (2008) Linear time maximally stable extremal regions. In: Forsyth,D. et al. (eds.) ECCV, pp. 183–196.

SUMMARY: ImgLib2 is an open-source Java library for n-dimensional data representation and manipulation with focus on image processing. It aims at minimizing code duplication by cleanly separating pixel-algebra, data access and data representation in memory. Algorithms can be implemented for classes of pixel types and generic access patterns by which they become independent of the specific dimensionality, pixel type and data representation. ImgLib2 illustrates that an elegant high-level programming interface can be achieved without sacrificing performance. It provides efficient implementations of common data types, storage layouts and algorithms. It is the data model underlying ImageJ2, the KNIME Image Processing toolbox and an increasing number of Fiji-Plugins. AVAILABILITY: ImgLib2 is licensed under BSD. Documentation and source code are available at http://imglib2.net and in a public repository at https://github.com/imagej/imglib. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics Online. CONTACT: [email protected]

Performance analysis in the field of camera-based simultaneous localisation and mapping (Visual SLAM, VSLAM) is still an unsolved problem. For VSLAM systems, there is a lack of generally accepted performance measures, test frameworks, and benchmark problems. Most researchers test by visually inspecting their systems on recorded image sequences, or measuring accuracy on simulated data of simplified point-cloud-like environments. Both approaches have their disadvantages. Recorded sequences lack ground truth. Simulations tend to oversimplify low-level aspects of the problem. In this paper, we propose to evaluate VSLAM systems on rendered image sequences. The intention is to move simulations towards more realistic conditions while still having ground truth. For this purpose, we provide a complete and extensible framework which addresses all aspects, from rendering to ground truth generation and automated evaluation. To illustrate the usefulness of this framework, we provide experimental results assessing the benefit of feature normal estimation and subpixel accurate matching on sequences with and without motion blur.

Flexibility and robustness of visual SLAM systems have been shown to benefit from an inverse depth parameterisation of features. However the increased number of 6 parameters per feature presents a problem to real-time EKF SLAM implementations because their computational complexity scales quadratically with the size of the state vector. Recent work tackles this for instance by converting the representation of well-established features from inverse to regular depth. In this paper, we propose a parameterisation where bundles of features share a common representation of the view-point they were initially observed from. According to the experiments performed, a feature occupies effectively about 1.5 state parameters in the proposed approach, allowing real-time performance for maps with more than 200 features. 1

In this paper, we describe a technique for the computation of augmented surface patches from stereo images. The stereo vision data is represented as patchlets, which are planar surface elements that have a position, surface normal, size, and confidence measures on the position and normal direction. To estimate the patchlet parameters, we use probabilistic image alignment. Whereas the patchlet generation method by Murray assumes the error on the disparity values to be equal for all pixels, we take the individual intensity patterns into account as well. This way, we obtain better estimates of the confidence measures in the patchlets. We describe the patchlet formation from the disparity and intensity values. To evaluate the quality of the results, we use real and synthetic images as they might be encountered in a typical mobile robot application. For a direct comparison, the images are processed both using Murray’s and our approach. 1