Martin Baatz

Object-oriented image analysis for high content screening: detailed quantification of cells and sub cellular structures with the Cellenger software.

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.

Cognition Network Technology: Object Orientation and Fractal Topology in Biomedical Image Analysis. Method and Applications

Data analysis in general and image analysis in particular require multi-scale approaches when dealing with complex structures. Relational information between structures on different scales needs to be taken into account. In many application fields, automated image interpretation still is a significant bottleneck due to the lack of appropriate image analysis technology. A new approach, Cognition Network Technology, is presented that was developed to handle and analyze complex data. This contribution focuses on how it handles and analyzes image data based on an object oriented, hierarchical and networked data model. A specific programming language allows building a semantic knowledge base that is used to interpreting image data by creating and processing instances of this data model. In many operational analysis tasks the approach has proven to produce reliable results fully automatically. It especially extracts structures of interest even in challenging cases such as low signal to noise ratio images, heterogeneous or variable structures of interest or tasks which include a complex semantic.

Bildanalyse in Medizin und Biologie

Heutzutage sind bildgebende Verfahren aus medizinischen Untersuchungen nicht mehr wegzudenken. Diverse Methoden – basierend auf dem Einsatz von Ultraschallwellen, Rontgenstrahlung, Magnetfeldern oder Lichtstrahlen – werden dabei spezifisch eingesetzt und liefern umfangreiches Datenmaterial uber den Korper und sein Inneres. Anhand von Mikroskopieaufnahmen aus Biopsien konnen daruber hinaus Daten uber die morphologische Eigenschaften von Korpergeweben gewonnen werden. Aus der Analyse all dieser unterschiedlichen Arten von Informationen und unter Konsultation weiterer klinischer Untersuchungen aus diversen medizinischen Disziplinen kann unter Berucksichtigung von Anamnesedaten ein „Gesamtbild“ des Gesundheitszustands eines Patienten erstellt werden. Durch die Flut der erzeugten Bilddaten kommt der Bildverarbeitung im Allgemeinen und der Bildanalyse im Besonderen eine immer wichtigere Rolle zu. Gerade im Bereich der Diagnoseunterstutzung, der Therapieplanung und der bildgefuhrten Chirurgie bilden sie Schlusseltechnologien, die den Forschritt nicht nur auf diesen Gebieten masgeblich vorantreiben.

Tissue Phenomics for prognostic biomarker discovery in low- and intermediate-risk prostate cancer

Tissue Phenomics is the discipline of mining tissue images to identify patterns that are related to clinical outcome providing potential prognostic and predictive value. This involves the discovery process from assay development, image analysis, and data mining to the final interpretation and validation of the findings. Importantly, this process is not linear but allows backward steps and optimization loops over multiple sub-processes. We provide a detailed description of the Tissue Phenomics methodology while exemplifying each step on the application of prostate cancer recurrence prediction. In particular, we automatically identified tissue-based biomarkers having significant prognostic value for low- and intermediate-risk prostate cancer patients (Gleason scores 6–7b) after radical prostatectomy. We found that promising phenes were related to CD8(+) and CD68(+) cells in the microenvironment of cancerous glands in combination with the local micro-vascularization. Recurrence prediction based on the selected phenes yielded accuracies up to 83% thereby clearly outperforming prediction based on the Gleason score. Moreover, we compared different machine learning algorithms to combine the most relevant phenes resulting in increased accuracies of 88% for tumor progression prediction. These findings will be of potential use for future prognostic tests for prostate cancer patients and provide a proof-of-principle of the Tissue Phenomics approach.

Pleiotropy and the Evolution of Adaptability

The systems approach to the investigation of organismic evolution is a conceptual framework introduced by Riedl in the 1970s (Riedl, 1977, 1978). One aim was to elucidate the relationship between certain evolutionary implications of morphological concepts, such as homology, and the population genetic approach of Modern Synthesis. At about the same time an intense scientific discussion began anew regarding the interpretation of macroevolution. Some scientists doubted the usefulness of the population genetic program to the area of macroevolution (Gould, 1980; Stanley, 1975). In fact, there are reasons in the mathematical structure of evolutionary theory: the formal apparatus of population genetics was created to describe elementary mechanisms of evolution, i.e. the distribution and change of gene frequencies, but not the process of evolution itself, i.e. the transformation of genotypic and phenotypic patterns of organization. Two statements, which at first glance seem to be incompatible, mark the positions in this discussion between microevolutionists and macroevolutionists: 1. There are no elementary mechanisms other than these offered by population genetics: mutation, duplication, selection, migration and random drift; every neo-Darwinian explanation must be based on these mechanisms. 2. Phenomena of macroevolution follow their own laws which cannot necessarily be derived from the principles of microevolution.