  We present a novel graph neural network (GNN) approach for cell tracking in
high-throughput microscopy videos. By modeling the entire time-lapse sequence
as a direct graph where cell instances are represented by its nodes and their
associations by its edges, we extract the entire set of cell trajectories by
looking for the maximal paths in the graph. This is accomplished by several key
contributions incorporated into an end-to-end deep learning framework. We
exploit a deep metric learning algorithm to extract cell feature vectors that
distinguish between instances of different biological cells and assemble same
cell instances. We introduce a new GNN block type which enables a mutual update
of node and edge feature vectors, thus facilitating the underlying message
passing process. The message passing concept, whose extent is determined by the
number of GNN blocks, is of fundamental importance as it enables the `flow' of
information between nodes and edges much behind their neighbors in consecutive
frames. Finally, we solve an edge classification problem and use the identified
active edges to construct the cells' tracks and lineage trees. We demonstrate
the strengths of the proposed cell tracking approach by applying it to 2D and
3D datasets of different cell types, imaging setups, and experimental
conditions. We show that our framework outperforms current state-of-the-art
methods on most of the evaluated datasets. The code is available at our
repository: https://github.com/talbenha/cell-tracker-gnn.
