Résumé : Bio-pharmaceutical industry is a vast growing market and recent recommendations of the Food and Drug Administration have put a large emphasis on the characterization of biological processes and models. As a consequence, there is a high incentive on developing modern sensors in order to more accurately monitor and control processes. In that way, Digital Holographic Microscopy (DHM) presents unique features thanks to the refocusing and quantitative phase contrast imaging capabilities. In this thesis we investigate the usage of DHM to monitor yeast cultures that are often used in both the bio-pharmaceutical and bread industries and lay the basis of a methodological framework for the study of in-line cell cultures in the context of process control. We begin with a description of Digital Holography and the microscopy setup used in the thesis as well as a detailed explanation of the image processing required to extract the holographic data and its implementation on GPU with some speed execution figures given for three popular programming paradigms. We then describe the flow setup used and infer the limitations on the dynamic range of the technique due to both Poisson statistics and overlapping phenomena. Finally, we describe an algorithm that extracts the cells position, count and morphological information such as the size, aspect ratio, circularity and refraction index. Some experimental results are presented for yeasts before drawing a general overview of the technology and its dependencies. We further end with some conclusions concerning the technology and a brief comparison with existing competitors.