Karina Ruzaeva successfully defended her dissertation
HDS-LEE congratulates Karina on her successful defense.
Her dissertation was on “Geometry-aware image analysis for microfluidic live-cell experimentation”.
The summary of her thesis is as follows:
The dissertation emphasizes the importance of incorporating prior knowledge of microorganisms' geometry and behavior—specifically their shape, size, and division mechanisms—into image analysis techniques. In this dissertation, we present an image processing workflow comprising ground truth data generation, segmentation, and tracking. This ground truth generation and subsequent segmentation and tracking algorithms are informed by the geometry and behavioral characteristics of cells, which are determined by the selected microorganism.
To implement this workflow, this dissertation proposes ground truth data generation methods that include both synthetic image simulations and the processing of annotations from real data. For the segmentation task, we combine geometry-aware variational spline-based segmentation with machine learning-based detection to enhance the accuracy of cell identification. This approach is complemented by activity-based tracking that monitors cell behavior over time, enabling the extraction of critical parameters such as cell size, count, and dynamic behavior. The extracted data can be used to dynamically adjust bioprocess conditions to optimize growth and yield, leading to greater efficiency and productivity in biotechnological processes.
By integrating this geometry and behavior-aware image processing methods, including ground truth generation, geometry-aware segmentation, and activity-based tracking, this dissertation underscores the potential of precise image analysis in enhancing live-cell experimentation. The results not only improve the accuracy of single-cell analysis but also help to optimize biotechnological processes by understanding cellular behavior.
