Hematology:

Blood provides many insights into the health of an organism, especially in the field of in vitro diagnostics. As a result, hematological analyses such as complete blood count (CBC) represent a large proportion of laboratory tests in the healthcare sector. Despite the availability of largely automated analyzers, the gold standard for routine diagnosis of hematological disorders is the laborious Giemsa-stained blood smear, which suffers from inter-observer variability. Other drawbacks of current approaches include high sample preparation effort, material cost, long processing time, or lack of clinical relevance. The CellFace project was initiated to address these issues and in particular to reduce time-consuming manual intervention. Flow cytometry combined with quantitative phase imaging allows researchers to capture 3D images of each individual cell while maintaining a high-throughput flow of blood cells. The overall goal is to establish this label- and reagent-free approach as a platform technology for automated blood cell diagnostics. The best methods to analyze the recorded phase images are still under investigation. Therefore, classical and modern techniques in the field of computer vision and machine learning will be investigated for their suitability to interpret the images and the information they contain.