## 5 Automated Analysis and QPI  
A promising potential for cellular imaging is the ability to use QPI to combine the non-invasive full-field imaging at short imaging intervals with automated analysis of spatio-temporal cell signatures, which enables the gathering of data from a large number of individual cells for long periods of time, typically several cell cycles [93]. Given a stable segmentation of cell area and tracking of cell position, it is possible to monitor morphological cell parameters over time, while at the same time recording cell position and movement. When phase shift images are segmented, a large number of cellular features related to morphology, density, texture, and motility can be calculated for each individual cell. The highly stochastic nature of cell phenotypes results in high variability between systems to distinguish between different cell types and cell stages. Comparisons are often only possible using population averages or large sample sets. Consequently, a successful analysis of individual cells will require some degree of automation. Cellular and tissue dynamics were measured nearly ten years ago by studying speckle fluctuations caused by cellular and sub-cellular motions [94; 95], and several studies have since investigated the characterization and separation of blood cells [96; 97]. Abnormalities of erythrocytes are important markers for disease. For example, [PERSON] et al. used QPI obtained by off-axis DH microscopy to automatically calculate many useful parameters of red blood cells, using algorithms to provide joint statistical distributions of the cell properties [98]. Simple and fast methods are needed urgently because morphology and functionality can differ quickly over time in a blood sample. [PERSON] et al. used digital in-line holographic microscopy combined with machine learning algorithms to classify different types of erythrocytes [51]. Recent advances in image analysis and machine learning classification algorithms applied to various types of medical imaging [99] have only just started to be applied to QPI. A typical approach for automated analysis consists of two major steps: The extraction of features from images, and the classification of cells into groups on the basis of the features. Studies have shown the ability of QPI to distinguish between cancer cell types in blood using relatively few basic features such as maximum and average intensity, and cell diameter [20; 100]. However, the majority of studies use more features. The inline-DH microscopy method used by [PERSON] et al. [19; 20] to achieve large-scale fingerprinting capabilities was useful for characterizing circulating tumor cells (CTC) in bulk flow. The authors were able to characterize tumor cell lines with different metastatic potential, and to distinguish drug resistant tumor cells from their normal counterparts. The first studies of classification of adherent cells were published in 2017 for applications of drug exposure [51] and cell cycle state [57], both relying on a few dozen simple features relating to size and shape (e.g., area, eccentricity, average, and maximum phase shift) and texture (e.g., energy, homogeneity, and entropy). Assigning a direct biological interpretation to QPI-derived features can be difficult as intracellular fluctuations in phase shift could be due to variations in cell thickness, as well as density or changes in internal organelle arrangement. Moreover, several features can be expected to show high correlation because they are all collected from the phase shift profile, thus providing redundant information. The clear benefit of studying adherent cells instead of cells in flow is the ability to simultaneously gather information on cellular morphology, migration, and motion over time. The time spent for each analysis will be limited by the expected speed of the analyzed process. [PERSON] et al. [57] recently identified 26 digital features referred to as quantitative digital holographic cytometry (DHC).