Runtime-Flexible Multi-dimensional Arrays and Views for C++98 and C++0x

Multi-dimensional arrays are among the most fundamental and most useful data structures of all. In C++, excellent template libraries exist for arrays whose dimension is fixed at runtime. Arrays whose dimension can change at runtime have been implemented in C. However, a generic object-oriented C++ implementation of runtime-flexible arrays has so far been missing. In this article, we discuss our new implementation called Marray, a package of class templates that fills this gap. Marray is based on views as an underlying concept. This concept brings some of the flexibility known from script languages such as R and MATLAB to C++. Marray is free both for commercial and non-commercial use and is publicly available from www.andres.sc/marrayComment: Free source code availabl

How to Extract the Geometry and Topology from Very Large 3D Segmentations

Segmentation is often an essential intermediate step in image analysis. A volume segmentation characterizes the underlying volume image in terms of geometric information--segments, faces between segments, curves in which several faces meet--as well as a topology on these objects. Existing algorithms encode this information in designated data structures, but require that these data structures fit entirely in Random Access Memory (RAM). Today, 3D images with several billion voxels are acquired, e.g. in structural neurobiology. Since these large volumes can no longer be processed with existing methods, we present a new algorithm which performs geometry and topology extraction with a runtime linear in the number of voxels and log-linear in the number of faces and curves. The parallelizable algorithm proceeds in a block-wise fashion and constructs a consistent representation of the entire volume image on the hard drive, making the structure of very large volume segmentations accessible to image analysis. The parallelized C++ source code, free command line tools and MATLAB mex files are avilable from http://hci.iwr.uni-heidelberg.de/software.phpComment: C++ source code, free command line tools and MATLAB mex files are avilable from http://hci.iwr.uni-heidelberg.de/software.ph

The Lazy Flipper: MAP Inference in Higher-Order Graphical Models by Depth-limited Exhaustive Search

This article presents a new search algorithm for the NP-hard problem of optimizing functions of binary variables that decompose according to a graphical model. It can be applied to models of any order and structure. The main novelty is a technique to constrain the search space based on the topology of the model. When pursued to the full search depth, the algorithm is guaranteed to converge to a global optimum, passing through a series of monotonously improving local optima that are guaranteed to be optimal within a given and increasing Hamming distance. For a search depth of 1, it specializes to Iterated Conditional Modes. Between these extremes, a useful tradeoff between approximation quality and runtime is established. Experiments on models derived from both illustrative and real problems show that approximations found with limited search depth match or improve those obtained by state-of-the-art methods based on message passing and linear programming.Comment: C++ Source Code available from http://hci.iwr.uni-heidelberg.de/software.ph

The Spectral Ocean Color Imager (SPOC) – An Adjustable Multispectral Imager

SPOC (SPectral Ocean Color) is a 3U small satellite mission that will use an adjustable multispectral imager to map sensitive coastal regions and off coast water quality of Georgia and beyond. SPOC is being developed by the University of Georgia’s (UGA) Small Satellite Research Laboratory (SSRL) through NASA’s Undergraduate Student Instrument Project (USIP). UGA is working with Cloudland Instruments to develop a small scale (\u3c 1000 \u3ecm3) multispectral imager, ranging from 400-850nm, for Earth science applications which will fly as part of the NASA CubeSat Launch Initiative. The project is UGA’s first satellite mission and is built by a team of undergraduates from a wide range of backgrounds and supervised by a multidisciplinary team of graduate students and faculty. Development, assembly, testing, and validation of the multispectral imager, as well integrating it into the satellite are all being done in house. At an orbit of 400 km the resulting images will be 90 km x 100 km in size, with a default spatial resolution and spectral resolution of 130 m and 4 nm, respectively

Signal Transduction in the Footsteps of Goethe and Schiller

The historical town of Weimar in Thuringia, the "green heart of Germany" was the sphere of Goethe and Schiller, the two most famous representatives of German literature's classic era. Not yet entirely as influential as those two cultural icons, the Signal Transduction Society (STS) has nevertheless in the last decade established within the walls of Weimar an annual interdisciplinary Meeting on "Signal Transduction – Receptors, Mediators and Genes", which is well recognized as a most attractive opportunity to exchange results and ideas in the field

ilastik: interactive machine learning for (bio)image analysis

We present ilastik, an easy-to-use interactive tool that brings machine-learning-based (bio)image analysis to end users without substantial computational expertise. It contains pre-defined workflows for image segmentation, object classification, counting and tracking. Users adapt the workflows to the problem at hand by interactively providing sparse training annotations for a nonlinear classifier. ilastik can process data in up to five dimensions (3D, time and number of channels). Its computational back end runs operations on-demand wherever possible, allowing for interactive prediction on data larger than RAM. Once the classifiers are trained, ilastik workflows can be applied to new data from the command line without further user interaction. We describe all ilastik workflows in detail, including three case studies and a discussion on the expected performance

Automated Detection and Segmentation of Synaptic Contacts in Nearly Isotropic Serial Electron Microscopy Images

We describe a protocol for fully automated detection and segmentation of asymmetric, presumed excitatory, synapses in serial electron microscopy images of the adult mammalian cerebral cortex, taken with the focused ion beam, scanning electron microscope (FIB/SEM). The procedure is based on interactive machine learning and only requires a few labeled synapses for training. The statistical learning is performed on geometrical features of 3D neighborhoods of each voxel and can fully exploit the high z-resolution of the data. On a quantitative validation dataset of 111 synapses in 409 images of 1948×1342 pixels with manual annotations by three independent experts the error rate of the algorithm was found to be comparable to that of the experts (0.92 recall at 0.89 precision). Our software offers a convenient interface for labeling the training data and the possibility to visualize and proofread the results in 3D. The source code, the test dataset and the ground truth annotation are freely available on the website http://www.ilastik.org/synapse-detection

Revised diagnostic criteria for neurofibromatosis type 1 and Legius syndrome: an international consensus recommendation

Purpose By incorporating major developments in genetics, ophthalmology, dermatology, and neuroimaging, to revise the diagnostic criteria for neurofibromatosis type 1 (NF1) and to establish diagnostic criteria for Legius syndrome (LGSS). Methods We used a multistep process, beginning with a Delphi method involving global experts and subsequently involving non-NF experts, patients, and foundations/patient advocacy groups. Results We reached consensus on the minimal clinical and genetic criteria for diagnosing and differentiating NF1 and LGSS, which have phenotypic overlap in young patients with pigmentary findings. Criteria for the mosaic forms of these conditions are also recommended. Conclusion The revised criteria for NF1 incorporate new clinical features and genetic testing, whereas the criteria for LGSS were created to differentiate the two conditions. It is likely that continued refinement of these new criteria will be necessary as investigators (1) study the diagnostic properties of the revised criteria, (2) reconsider criteria not included in this process, and (3) identify new clinical and other features of these conditions. For this reason, we propose an initiative to update periodically the diagnostic criteria for NF1 and LGSS

Proteomic Fingerprints Distinguish Microglia, Bone Marrow, and Spleen Macrophage Populations

Mononuclear phagocytes (MP; dendritic cells, monocytes, tissue macrophages, and microglia) maintain tissue homeostasis and provide a first line of defense against invading pathogens. In specific circumstances, MPs also induce inflammatory responses and as such affect disease onset and progression. Despite intensive research into MP biology, little is known of the functional and molecular properties of individual MP subtypes. Using a novel proteomics platform, unique protein patterns and protein identities were observed among populations of spleen and bone marrow macrophages and microglia. Cells were obtained from C57BL/6 mice and were cultivated in macrophage colony-stimulating factor. MP subtypes were indistinguishable by morphological or antigenic criteria. Protein profiling by Surface Enhanced Laser Desorption Ionization-Time of Flight (SELDI-TOF) ProteinChip® assays with weak cationic exchange chips showed unique MP spectral profiles. Corresponding protein fractions were recovered by high performance liquid chromatography and identified by liquid chromatography tandem mass spectrometry. The results provide a unique means to distinguish microglia from other MP subtypes