Plugin Development for Single-cell Tracking

One of the significant challenges of single-cell biology is to provide quantitative descriptions of cell behavior over time. Recent advances in machine learning-based instance segmentation of cells in microscopy image data has greatly improved our ability to gain biomedical insights. Linking these observations over time to extract reliable cell trajectories and derive lineage information across several generations is key. Having fully annotated cell trajectories will enable scientists to understand how cellular systems evolve over time in development and disease.

With several established technologies for single-cell tracking, this project will integrate these tools with napari. The project aims to build upon the existing technologies to create a suite of interoperable tools and plugins that enable the next generation of single-cell biology. These plugins will ideally be combined with others in the napari ecosystem, such as those from the recent napari Alfa Cohort.