A simple tool for stereological assessment of digital images: the STEPanizer

STEPanizer is an easy-to-use computer-based software tool for the stereological assessment of digitally captured images from all kinds of microscopical (LM, TEM, LSM) and macroscopical (radiology, tomography) imaging modalities. The program design focuses on providing the user a defined workflow adapted to most basic stereological tasks. The software is compact, that is user friendly without being bulky. STEPanizer comprises the creation of test systems, the appropriate display of digital images with superimposed test systems, a scaling facility, a counting module and an export function for the transfer of results to spreadsheet programs. Here we describe the major workflow of the tool illustrating the application on two examples from transmission electron microscopy and light microscopy, respectively.

Differential block coding of bilevel images

Abstract- In this correspondence, a simple one-dimensional (1-D) differencing operation is applied to bilevel images prior to block coding to produce a sparse binary image that can be encoded efficiently using any of a number of well-known techniques. The difference image can be encoded more efficiently than the original bilevel image whenever the average run length of black pixels in the original image is greater than two. Compression is achieved because the correlation between adjacent pixels is reduced compared with the original image. The encoding/decoding operations are described and compression performance is presented for a set of standard bilevel images.

Automatic extraction of the mid-facial plane for cranio-maxillofacial surgery planning

Recently developed computer applications provide tools for planning cranio-maxillofacial interventions based on 3-dimensional (3D) virtual models of the patient's skull obtained from computed-tomography (CT) scans. Precise knowledge of the location of the mid-facial plane is important for the assessment of deformities and for planning reconstructive procedures. In this work, a new method is presented to automatically compute the mid-facial plane on the basis of a surface model of the facial skeleton obtained from CT. The method matches homologous surface areas selected by the user on the left and right facial side using an iterative closest point optimization. The symmetry plane which best approximates this matching transformation is then computed. This new automatic method was evaluated in an experimental study. The study included experienced and inexperienced clinicians defining the symmetry plane by a selection of landmarks. This manual definition was systematically compared with the definition resulting from the new automatic method: Quality of the symmetry planes was evaluated by their ability to match homologous areas of the face. Results show that the new automatic method is reliable and leads to significantly higher accuracy than the manual method when performed by inexperienced clinicians. In addition, the method performs equally well in difficult trauma situations, where key landmarks are unreliable or absent.

Multiple Lab Comparison of Microarray Platforms

Microarray technology is a powerful tool able to measure RNA expression for thousands of genes at once. Various studies have been published comparing competing platforms with mixed results: some find agreement, others do not. As the number of researchers starting to use microarrays and the number of crossplatform meta-analysis studies rapidly increase, appropriate platform assessments become more important. Here we present results from a comparison study that offers important improvements over those previously described in the literature. In particular, we notice that none of the previously published papers consider differences between labs. For this paper, a consortium of ten labs from the Washington DC/Baltimore (USA) area was formed to compare three heavily used platforms using identical RNA samples: Appropriate statistical analysis demonstrates that relatively large differences exist between labs using the same platform, but that the results from the best performing labs agree rather well. Supplemental material is available from http://www.biostat.jhsph.edu/~ririzarr/techcomp/

On the Synthesis of Microarray Experiments

With many different investigators studying the same disease and with a strong commitment to publish supporting data in the scientific community, there are often many different datasets available for any given disease. Hence there is substantial interest in finding methods for combining these datasets to provide better and more detailed understanding of the underlying biology. We consider the synthesis of different microarray data sets using a random effects paradigm and demonstrate how relatively standard statistical approaches yield good results. We identify a number of important and substantive areas which require further investigation.

Automatic Reduction of Artifacts in EEG-Signals

Electroencephalograms (EEG) are often contaminated with high amplitude artifacts limiting the usability of data. Methods that reduce these artifacts are often restricted to certain types of artifacts, require manual interaction or large training data sets. Within this paper we introduce a novel method, which is able to eliminate many different types of artifacts without manual intervention. The algorithm first decomposes the signal into different sub-band signals in order to isolate different types of artifacts into specific frequency bands. After signal decomposition with principal component analysis (PCA) an adaptive threshold is applied to eliminate components with high variance corresponding to the dominant artifact activity. Our results show that the algorithm is able to significantly reduce artifacts while preserving the EEG activity. Parameters for the algorithm do not have to be identified for every patient individually making the method a good candidate for preprocessing in automatic seizure detection and prediction algorithms.

Systemic analysis of microarray data sets towards identifying genes regulating root development

Nitrogen and water are essential for plant growth and development. In this study, we designed experiments to produce gene expression data of poplar roots under nitrogen starvation and water deprivation conditions. We found low concentration of nitrogen led first to increased root elongation followed by lateral root proliferation and eventually increased root biomass. To identify genes regulating root growth and development under nitrogen starvation and water deprivation, we designed a series of data analysis procedures, through which, we have successfully identified biologically important genes. Differentially Expressed Genes (DEGs) analysis identified the genes that are differentially expressed under nitrogen starvation or drought. Protein domain enrichment analysis identified enriched themes (in same domains) that are highly interactive during the treatment. Gene Ontology (GO) enrichment analysis allowed us to identify biological process changed during nitrogen starvation. Based on the above analyses, we examined the local Gene Regulatory Network (GRN) and identified a number of transcription factors. After testing, one of them is a high hierarchically ranked transcription factor that affects root growth under nitrogen starvation. It is very tedious and time-consuming to analyze gene expression data. To avoid doing analysis manually, we attempt to automate a computational pipeline that now can be used for identification of DEGs and protein domain analysis in a single run. It is implemented in scripts of Perl and R.