Scientific Papers - Life Sciences and Medical Imaging

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2006

Athelogou M, SchmidtG, Schäpe A, Urbani M, Leiderer R, Meissner H, Diebold J, Messmer K, Binnig G: Multi-Resolution Quantification of Liver Morphology using Different Image Modalities Poster presented at SBMC 2006 - Conference on Systems Biology of Mammalian Cells, HEPATOSYS 2006 in Heidelberg

Baatz M, Arini N, Schaepe A, Binnig G, Linssen B: Object-Oriented Image Analysis for High Content Screening: Detailed Quantification of Cells and Sub Cellular Structures with the Cellenger Software Background: Detailed image analysis still is a considerable bottleneck for many cellular assays, and automated solutions to the problem are desirable. However, dealing with the complexity and variability of structures in cellular images makes detailed and reliable analysis a nontrivial task. Methods: Therefore, based on the object-oriented image analysis approach, a novel image analysis technology, a flexible and reliable system for image analysis in cellular assays was developed. It contains a library of predefined, adaptable modules, each of them developed for a specific analysis task. The system can be configured easily by combining appropriate modules and adapting them interactively to the specific image data, if necessary. By representing cells and sub cellular structures within a network of interlinked image objects, a large number of parameters can be derived that describe shape, intensity, and relevant structural and relational aspects of any chosen class of structures. Results: Thus, multi-parameter analysis and multiplexing are supported. A sample application based on this approach demonstrates that GFP signals can be distinguished based on their properties and the relative location within the cell. (In: Special Issue: Slide-Based Cytometry in Cytomics, volume 69A, issue 7, 2006, pp 652 - 658)

Kriete A, Papazoglou E, Apazoglou B, Edrissi B, Pais H, Pourrezaei K : Automated quantification of quantum-dot-labelled epidermal growth factor receptor internalization via multiscale image segmentation The ability to monitor epidermal growth factor receptor (EGFr) internalization specifically, and cellular protein concentrations and activation states in general, has been recently improved by the use of appropriately functionalized quantum dots (QDs), as a result of the long-lasting fluorescence, brightness and multicolour of these nanoparticles. However, important quantitative information about locational proteomics is based on the analysis of the properties of many cells and cell cultures on a per-cell basis, rather than tracking individual events within one cell. Moreover, relative positional information is often gained from traditional staining protocols of distinct cellular compartments that are prone to noise, fading and low contrast. We apply a novel multiscale image segmentation based on region growing to classify automatically objects in fixed cell preparations and to define regional zones in all cells prior to QD concentration measures. This allows rapid quantitative description of EGFr internalization as it changes with incubation time. The capabilities realizable by simultaneous application of confocal imaging and functionalized QDs in conjunction with advanced image analysis are a prerequisite for automated and multiplexed cytomics assays. (In: Journal of Microscopy, Vol. 222, Pt 1 April 2006, pp. 22–27)

Schönmeyer R, Prvulovica D, Rotarska-Jagielaa A, Haenschel C, Linden DEJ: Automated segmentation of lateral ventricles from human and primate magnetic resonance images using cognition network technology. Automatic segmentation of different types of tissue from magnetic resonance images is of great importance for clinical and research applications, particularly large-scale and longitudinal studies of brain pathology. We developed a fully automated algorithm for the segmentation of lateral ventricles from cranial magnetic resonance images. This problem is of interest in the study of schizophrenia, dementia and other neuropsychiatric disorders. Our algorithm achieves comparable results to expert human raters. The theoretical approach, which is based on an emerging object-oriented technology that has been adapted and evaluated to process 3D data for the first time, may, in the future, be transferred to other important problems of magnetic resonance image analysis like gray/white matter segmentation. (In: Magnetic Resonance Imaging, Volume 24, Issue 10, December 2006, pp. 1377–1387; D 2006 Elsevier Inc. All rights reserved)

Schönmeyer R, Rotarska-Jagiela A, Prvulovic D, Haenschel C, Linden DEJ: Fully automated segmentation of white matter above lateral ventricles from magnetic resonance images of the human brain Poster

Schönmeyer R, Rotarska-Jagiela A, Prvulovic D, Haenschel C, Linden DEJ (GERMAN version only): Vollautomatische Segmentierung der weissen Hirnsubstanz oberhalb der Seitenventrikel aus kernspintomographischen Datensätzen In dieser Arbeit stellen wir einen vollautomatisierten Algorithmus vor, der in der Lage ist, die weiße Substanz in der Region oberhalb der Ventrikel aus T1-gewichteten anatomischen MR-Datensätzen des menschlichen Gehirns zu segmentieren. Im Gegensatz zu anderen Methoden werden dabei nur wenige Voraussetzungen Äuber die zu verarbeitenden Daten gemacht, und es sind keine ggf. manuell zu begleitenden Vorverarbeitungsschritte, wie z.B. Filterung und/oder Normalisierung, nötig. Der Algorithmus wurde unter Zuhilfenahme der Definiens Cognition Network Technology implementiert, die eine wissensbasierte und kontextsensitive Handhabung der Bilddaten erlaubt. Eine quantitative Auswertung anhand von bis jetzt 10 Datensätzen zeigt, daß im Vergleich zu einer manuellen Segmentierung eine overlay-metric von 0.9 erzielt wird. (Published in: Handels H, Ehrhardt J, Horsch A, Meinzer H-P, Tolxdorff T, ed. Informatik aktuell: Bildverarbeitung für die Medizin (BVM); 2006; Heidelberg, Germany: Springer-Verlag, Berlin Heidelberg, 2006: 146-150)

Schmidt G, Schäpe A, Binnig G, Urbani M, Leiderer R, Athelogou M: Definiens Cognition Network Technology for Automated and Holistic Analysis of Liver Image Data Poster at the Conference of Systems Biology of Mammalian Cells, SBMC 2006, July 12 - 14, 2006, Heidelberg, Germany.

Schmidt G, Schönmeyer R, Meissner H, Diebold J, Athelogou, M: Definiens Cognition Network Technology for Automated Analysis of Liver Tissue Images Poster at the Society for Biomolecular Sciences' 12th Annual Conference, September 17-21, 2006, Seattle, Washington, USA.

Warford, T: Mapping gene expression at the cellular level The completion of gene sequencing for Homo sapiens and several other species has underlined the importance of transcription and translation in generating and maintaining the complex molecular structures of individual cells and tissues. Investigative procedures such as complementary DNA (cDNA) microarray analysis, real-time polymerasechain reaction (PCR), mass spectrometry and blotting methods are providing vital information on gene expression at the molecular level. (In:The Biomedical Scientist, October 2006, pp 870-872)

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