Detection and tracking of cells and focal adhesions in microscopy images

Modern microscopy and imaging techniques have enabled cell biologists to observe and record cell processes in detail, thus generating high volumes of imaging data. Meaningful conclusions can only be drawn from such big datasets using automated quantitative analysis. <br><br>This thesis focuses on ways to automatically perform detection, segmentation, and tracking of cells or subcellular structures in time-lapse microscopy image sequences to facilitate efficient analysis of these datasets.

Furthermore, a secondary purpose of the thesis deals with the lack of annotated data in the biomedical field. <br><br>In order to achieve these goals, we propose the use of probabilistic tracking algorithms to accurately and robustly track either migrating cells or their internal structures. For example, focal adhesions moving inside a single migrating normal human epidermal keratinocyte. In particular, the use of the Interacting Multiple Model (IMM) algorithm is proposed given its ability to take into account several different motion models at every given time.