Fully Automatic Segmentation of Brain Tumor Images using Support Vector Machine Classiffication in Combination with Hierarchical Conditional Random Field Regularization

Delineating brain tumor boundaries from magnetic resonance images is an essential task for the analysis of brain cancer. We propose a fully automatic method for brain tissue segmentation, which combines Support Vector Machine classification using multispectral intensities and textures with subsequent hierarchical regularization based on Conditional Random Fields. The CRF regularization introduces spatial constraints to the powerful SVM classification, which assumes voxels to be independent from their neighbors. The approach first separates healthy and tumor tissue before both regions are subclassified into cerebrospinal fluid, white matter, gray matter and necrotic, active, edema region respectively in a novel hierarchical way. The hierarchical approach adds robustness and speed by allowing to apply different levels of regularization at different stages. The method is fast and tailored to standard clinical acquisition protocols. It was assessed on 10 multispectral patient datasets with results outperforming previous methods in terms of segmentation detail and computation times.

Automated detection of the osseous acetabular rim using three-dimensional models of the pelvis

An automated algorithm for detection of the acetabular rim was developed. Accuracy of the algorithm was validated in a sawbone study and compared against manually conducted digitization attempts, which were established as the ground truth. The latter proved to be reliable and reproducible, demonstrated by almost perfect intra- and interobserver reliability. Validation of the automated algorithm showed no significant difference compared to the manually acquired data in terms of detected version and inclination. Automated detection of the acetabular rim contour and the spatial orientation of the acetabular opening plane can be accurately achieved with this algorithm.

Automated Portal Placement for Arthroscopic Hip Surgery

Placing portal incisions during arthroscopic hip surgery presents challenges for surgeons in terms of anatomic accessibility and patient safety. Based on key anatomic landmarks and portal placement information from recent literature, suggested portal incisions were determined. Guidance in the placement of the three most common portal incision locations (anterior, anterolateral, and posterolateral) for arthroscopic surgery; in addition to visual feedback on tool trajectory to the hip joint is provided in real time by a computer aided system for hip arthroscopy. By simplifying the portal placement process, one of the most challenging aspects of arthroscopic hip surgery, an increased use of this minimally invasive technique could be possible. In addition to portal information, improvements to an existing computer aided system for arthroscopic hip surgery, including a new hip model and redesigned mechanical tracking linkage, were completed.

Time course based artifact identification for independent components of resting-state FMRI

In functional magnetic resonance imaging (fMRI) coherent oscillations of the blood oxygen level-dependent (BOLD) signal can be detected. These arise when brain regions respond to external stimuli or are activated by tasks. The same networks have been characterized during wakeful rest when functional connectivity of the human brain is organized in generic resting-state networks (RSN). Alterations of RSN emerge as neurobiological markers of pathological conditions such as altered mental state. In single-subject fMRI data the coherent components can be identified by blind source separation of the pre-processed BOLD data using spatial independent component analysis (ICA) and related approaches. The resulting maps may represent physiological RSNs or may be due to various artifacts. In this methodological study, we propose a conceptually simple and fully automatic time course based filtering procedure to detect obvious artifacts in the ICA output for resting-state fMRI. The filter is trained on six and tested on 29 healthy subjects, yielding mean filter accuracy, sensitivity and specificity of 0.80, 0.82, and 0.75 in out-of-sample tests. To estimate the impact of clearly artifactual single-subject components on group resting-state studies we analyze unfiltered and filtered output with a second level ICA procedure. Although the automated filter does not reach performance values of visual analysis by human raters, we propose that resting-state compatible analysis of ICA time courses could be very useful to complement the existing map or task/event oriented artifact classification algorithms.

Genome-Wide Survey of Pseudogenes in 80 Fully Re-sequenced Arabidopsis thaliana Accessions

Pseudogenes (Ψs), including processed and non-processed Ψs, are ubiquitous genetic elements derived from originally functional genes in all studied genomes within the three kingdoms of life. However, systematic surveys of non-processed Ψs utilizing genomic information from multiple samples within a species are still rare. Here a systematic comparative analysis was conducted of Ψs within 80 fully re-sequenced Arabidopsis thaliana accessions, and 7546 genes, representing ~28% of the genomic annotated open reading frames (ORFs), were found with disruptive mutations in at least one accession. The distribution of these Ψs on chromosomes showed a significantly negative correlation between Ψs/ORFs and their local gene densities, suggesting a higher proportion of Ψs in gene desert regions, e.g. near centromeres. On the other hand, compared with the non-Ψ loci, even the intact coding sequences (CDSs) in the Ψ loci were found to have shorter CDS length, fewer exon number and lower GC content. In addition, a significant functional bias against the null hypothesis was detected in the Ψs mainly involved in responses to environmental stimuli and biotic stress as reported, suggesting that they are likely important for adaptive evolution to rapidly changing environments by pseudogenization to accumulate successive mutations.