  Tracking all nuclei of an embryo in noisy and dense fluorescence microscopy
data is a challenging task. We build upon a recent method for nuclei tracking
that combines weakly-supervised learning from a small set of nuclei center
point annotations with an integer linear program (ILP) for optimal cell lineage
extraction. Our work specifically addresses the following challenging
properties of C. elegans embryo recordings: (1) Many cell divisions as compared
to benchmark recordings of other organisms, and (2) the presence of polar
bodies that are easily mistaken as cell nuclei. To cope with (1), we devise and
incorporate a learnt cell division detector. To cope with (2), we employ a
learnt polar body detector. We further propose automated ILP weights tuning via
a structured SVM, alleviating the need for tedious manual set-up of a
respective grid search. Our method outperforms the previous leader of the cell
tracking challenge on the Fluo-N3DH-CE embryo dataset. We report a further
extensive quantitative evaluation on two more C. elegans datasets. We will make
these datasets public to serve as an extended benchmark for future method
development. Our results suggest considerable improvements yielded by our
method, especially in terms of the correctness of division event detection and
the number and length of fully correct track segments. Code:
https://github.com/funkelab/linajea
