Scientific Papers - Life Sciences

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2008

Athelogou M, Feehan O, Schönmeyer R, Binnig G: Automatische Analyse mehrdimensionaler Bilddaten Um Morphologie und Funktionen von Zellorganellen, Zellen, Organen oder Organismen besser zu verstehen, müssen Bilddaten zuverlässig und reproduzierbar analysiert und interpretiert werden. Definiens hat auf Grundlage der Computersprache Cognition Network Language eine neues Bildanalyseverfahren entwickelt, das Bilddaten automatisch und kontextbasiert analysiert. (Article published in LaborPraxis, Mai 2008 - GERMAN version only)

Athelogou M, Schönmeyer R, Schmidt G, Schäpe A, Baatz M, Binnig G : Bildanalyse in Medizin und Biologie - Beispiele und Anwendungen (GERMAN version only): Heutzutage sind bildgebende Verfahren aus medizinischen Untersuchungen nicht mehr wegzudenken. Der Einsatz von Ultraschallwellen, Röntgen, Magnetfeldern und Lichtstrahlen liefet umfangreiches Datenmaterial über den Körper und sein Inneres. Anhand von Mikroskopieaufnahmen aus Biopsien werden Daten über die morphologische Eigenschaften von Körpergeweben gewonnen. Aus der Analyse dieser unterschiedlichen Arten von Informationen und der Konsultation weiterer klinischer Untersuchungen kann unter Berücksichtigung von Anamnesedaten ein „Gesamtbild“ des Gesundheitszustands eines Patienten erstellt werden... (In: Medizintechnik - Life Science Engineering, by Wintermantel E und Suk-Woo Ha, ISBN: 978-3-540-74924-0, 4. überarb. u. erw. Aufl., Springer-Verlag Berlin Heidelberg, 2008, pp 983-1008) 

Athelogou M, Schmidt G, Schäpe A, Urbani M, Leiderer R, Meissner H, Diebold J, Messmer K, Binnig G : Definiens Cognition Network Technology for multi-modal Quantification of Liver Morphology Detailed knowledge about morphology of a biological system gives valuable and precious information  about its function and dynamics. Advantages in imaging technologies enable users to acquire thousands of images of different modalities and different levels of resolution on a specific biological system such as the liver. Sub cellular structures, cells, cell groups, tissue and the whole liver can be automatically analyzed, measured and quantified using image analysis software. The extracted image information on different scales of resolution can be transformed into valuable knowledge. We present such an example for liver using the context-based image analysis platform Definiens Enterprise Image Intelligence™ Suite, Electron micrographs, fluorescence- and light microscopy and CT-images from liver are analyzed within the same software. This multi-resolution, multi-modal quantification of the liver morphology opens new perspectives in using multi-scale knowledge simultaneously for systems biology purposes like modeling and simulation. (Poster)

Athelogou M, Schmidt G, Schönmeyer R, Feehan O, Sittek H, Binnig G: Definiens Cognition Network Technology for information centric Healthcare in Mammography Breast cancer detection and diagnosis systems are developed only for one data modality, for example only for x-ray mammography or mammasonography or MR-mammography. Each of these modalities has advantages and disadvantages concerning high specificity for breast cancer detection. Therefore radiologists consult different modalities, analyze them and correlate findings in order to perform high accuracy diagnosis of breast cancer. Although recent studies endorse multi modal breast diagnosis and screenings, there are no CAD systems, which allow computer aided detection and diagnosis by analyzing different mammography data modalities at once. We present a new technology and corresponding prototypes for such automated “multimodal” CAD systems. Different projections and representations of the same or of different breast lesions in the same or in different images of the same or of different image modalities can be automatically detected, classified, and linked and correlated to each other. Corresponding tables of statistical data concerning single or grouped lesions are automatically extracted and are used in relation to the patients demographic and anamnesis data for diagnostic purposes. (Poster)

Baatz M, Zimmermann J, Crawford S, Blackmore C: Detailed quantification of biomarker expression in the context of relevant morphological units The application of novel drugs requires appropriate biomarkers which help to personally target therapeutic decisions. Therefore, biomarkers, especially in multiplexed applications, play an ever increasing role in research as well as drug development with the goal to develop effective methods for patient stratification. Up to now, the detailed and reliable automated detection of structural units in tissue has been a challenging task and often fails. Definiens Cognition Network Technology® addresses this image analysis bottleneck with a novel approach. The method uses semantic object-networks and iteratively evaluates local context in order to solve image analysis problems. (poster presented at AACR Annual Meeting 2008, Apr 12 - 16, 2008, San Diego, CA)

Di Daniel E, Patel K, Zenger-Landolt B, Schreiber J, Blackmore C, Maycox P: Complex image-based cell biology endpoint analysis for drug discovery using Definiens software The Growth Cone (GC) is a specialized structure found at the tip of a neurite, which integrates information from the surrounding environment resulting in GC spreading or collapse. The GC assay measures the GC area of sensory neurons cultured as explants. This assay seems to be predictive of mood-stabilizing activity, as each of the mood stabilizers, lithium chloride, valproic acid and carbamazepine, increases GC area and decreases the percentage of collapsed sensory neuron GCs. This assay is, therefore, currently used to identify and validate potential novel targets for bipolar disorder. (Poster developed by GlaxoSmithKline and Defíniens)

Feehan O, Athelogou M, Schmidt G, Binnig G: Context-Based analysis of multidimensional, experimental and simulated image data Multidimensional image data arises frequently as an output of experimental processes in contemporary biology. Extracting knowledge from these images through image analysis can lead to the discovery of new information either directly or by providing parameters for further experiments or simulations. The presented work focuses on the analysis of experimental and simulation results involving the process of endocytosis in the hepatocytes. (Poster presented at SBMC 2008, Dresden, Germany, May 22, 2008) 

Rotarska-Jagiela A, Schönmeyer R, Oertela V, Haenschel C, Vogeley K, Linden D: The corpus callosum in schizophrenia - volume and connectivity changes affect specific regions The corpus callosum (CC) is of great interest for pathophysiological models of schizophrenia. Volume and structural integrity of the CC have been examined by volumetric and diffusion tensor imaging (DTI) studies, but results were not consistent across methods or studies. A possible explanation may be varying methodologies and accuracy of measurements based on a single slice or small regions of interest. In addition, none of the studies examined volume and diffusion values in the same group of patients, and thus the relationship between these anatomical measures is not clear. We used an automatic algorithm to segment seven midline slices of the CC from DTI images. We compared volume and the DTI measures fractional anisotropy (FA) and mean diffusivity (MD) in the CC and its subdivisions in the schizophrenia patients and matched controls. Patients had decreased volume, decreased FA and increased MD of the whole CC. The important novel finding is, however, that not all regions were equally affected by anatomical changes. The results emphasize the importance of using different methods in evaluation of white matter (WM) in schizophrenia to avoid false negative findings. In addition, the measures were highly correlated with each other, implying a common pathological process influencing FA, MD and volume of the CC. Although we cannot rule out other mechanisms affecting volume, FA and MD, converging evidence from cytoarchitectonic and genetic studies suggests that WM changes observed in schizophrenia may involve disintegration of healthy, functional axons and strengthening of aberrant connections resulting in increased severity of clinical symptoms. (In: NeuroImage, Volume 39, Issue 4,  February 15, 2008, pp 1522-1532) 

Schönmeyer R, Athelogou M, Rotarska-Jagiela A, Haenschel C, Linden D: Fully automated Segmentation of Corpus Callosum from Sagittal MRT Images In this contribution, we present a fully automated algorithm which is capable of segmenting a shape of corpus callosum (CC) from sagittal T1-weighted MRT images of the human brain. Our algorithm is based on the so-called Definiens Cognition Network Technology® from Definiens and extends this framework with a workflow to allow for a maximum of automatization including the possibility of visual inspection and ergonomical correction of segmentation errors if needed. The Definiens Cognition Network Technology™ is an context-based approach to efficiently create automated algorithms for con text-related analysis and segmentation of images incorporating expert knowledge which already has established applications in such different fields as analysis of data originating from remote-sensing or microscopic images from tissue samples. (Poster)

Schmidt G, Athelogou M, Schönmeyer R, Binnig G: Definiens Cognition Network Technology for fully automated 3D Segmentation of Liver The precise measurement of shape and composition of liver is a basis for diagnosis, surgery planning and therapy control. Since manual measurement of three dimensional structures is extremely  time consuming, cost intensive and subjective, automated methods are required in today’s challenging  clinical environment. Due to a large variability in appearance and shape, and many potential damages such as alcoholic cirrhosis and liver tumor, the automated reliable liver segmentation still represents a nontrivial task. Other CT image analysis difficulties arise because of insufficient separation from adjacent organs such as kidney, heart, and muscles, and due to time and liver state dependent effects of the contrast agent. We applied the Definiens Cognition Network Technology® on liver segmentation within a fully automated generation of a personal anatomical model. The liver segmentation results are evaluated on a set of 20 manually annotated CT data sets using the performance metrics volumetric overlap error and mean surface distance. (Poster)

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