  The presented algorithms for segmentation and tracking follow a 3-step
approach where we detect, track and finally segment nuclei. In the
preprocessing phase, we detect centroids of the cell nuclei using a
convolutional neural network (CNN) for the 2D images and a
Laplacian-of-Gaussian Scale Space Maximum Projection approach for the 3D data
sets. Tracking was performed in a backwards fashion on the predicted seed
points, i.e., starting at the last frame and sequentially connecting
corresponding objects until the first frame was reached. Correspondences were
identified by propagating detections of a frame t to its preceding frame t-1
and by combining redundant detections using a hierarchical clustering approach.
The tracked centroids were then used as input to variants of the seeded
watershed algorithm to obtain the final segmentation.
