Abstract
Biomedicine has seen tremendous advances in the field of image acquisition. The generation of digital images of high information content has become so straightforward and efficient that the volume of images accumulating in biomedical disciplines is posing significant challenges. Until now, conventional image analysis solutions are generally pixel-based and limited in the amount of information that they extract. However, a software system enabling the complex analysis of biomedical images should not impose restrictions on detection, classification and quantification of structures, but rather allow unlimited freedom to answer exhaustively all conceivable questions about the interactions and relationships between structures. Crucial to this is the precise and robust segmentation of relevant structures in digital micrographs. This challenge involves bringing structure, morphology and context into play. Based on the Definiens Cognition Network Technology®, solutions have been deployed for use in biomedicine. The technology is object-oriented, multi-scale, context-driven and knowledge-based. Images are interpreted on the properties of networked image objects, which results in numerous advantages. This approach enables users to bring in detailed expert knowledge and enables complex analyses to be performed with unprecedented accuracy, even on poor quality data or for structures exhibiting heterogeneous properties or variable phenotypes. Extracted structures are the basis for detailed morphometric, structural and relational measurements which can be exported for each individual structure. These data can be used for decision support or correlated against experimental or molecular data, thus bridging classical biomedicine with molecular biology. An overview of the technology is provided with examples from different biomedical applications.
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