Automated High Content Analysis of Multidimensional Image Data of Cells and Tissue

摘要

Automated digital microscopy is established in biology, pathology, and in drug discovery and development as a means to screen cell assays and tissue slides in high content and high throughput workflows for target identification and validation. It is further employed for analysis of tissue slides from biopsies. Simulations are also used in medical and systems biology to produce alphanumeric data that represents the structure, morphology, behavior and interactions of biological objects such as cells, cell organelles and proteins. Multidimensional digital image data is generated, such as multilayer con-focal imagery or alphanumeric simulation results, so as to span both domains, with simulation results forming an input for experimentation and vice versa. In order to understand the structure, function and dynamics of proteins, cell organelles, cells, tissue, organs etc. detailed morphological quantification of objects within these images is needed. We present application results of an object- and context-based image analysis method, which employs domain-knowledge and context information to analyze multidimensional image data automatically.