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The presented algorithms for segmentation and tracking follow a 3-stepapproach where we detect, track and finally segment nuclei. In thepreprocessing phase, we detect centroids of the cell nuclei using aconvolutional neural network (CNN) for the 2D images and aLaplacian-of-Gaussian Scale Space Maximum Projection approach for the 3D datasets. Tracking was performed in a backwards fashion on the predicted seedpoints, i.e., starting at the last frame and sequentially connectingcorresponding objects until the first frame was reached. Correspondences wereidentified by propagating detections of a frame t to its preceding frame t-1and by combining redundant detections using a hierarchical clustering approach.The tracked centroids were then used as input to variants of the seededwatershed algorithm to obtain the final segmentation.

Algorithms used for the Cell Segmentation Benchmark Competition at ISBI 2019 by RWTH-GE