Development of a Complete Minuscule Microscope: Embedding Data Pipeline and Machine Learning Segmentation

Sammanfattning: Cell culture is a fundamental procedure in many laboratories and precedes much research performed under the microscope. Despite the significance of this procedural stage, the monitoring of cells throughout growth is impossible due to the absence of equipment and methodological approaches. This thesis presents a low-cost, power-effective and versatile microscope with small enough dimensions to operate inside an incubator. Besides image acquisition, the microscope comprises other functions such as a data pipeline, implemented to save the images on the user’s computer via a server whilst also offering storage of the images on an integrated micro SD-card. Furthermore, a machine learning algorithm with a human-in-the-loop approach has been trained to segment the acquired images for cell proliferation and cell apoptosis tracking, and yielded promising results with an accuracy of 94%. For comparison, conventional segmentation techniques using operations such as the watershed function were deployed.The microscope described is versatile in operation as it offers the user to utilise one or more functions, depending on the purpose of the imaging.