  U-Net and its variants have been demonstrated to work sufficiently well in
biological cell tracking and segmentation. However, these methods still suffer
in the presence of complex processes such as collision of cells, mitosis and
apoptosis. In this paper, we augment U-Net with Siamese matching-based tracking
and propose to track individual nuclei over time. By modelling the behavioural
pattern of the cells, we achieve improved segmentation and tracking
performances through a re-segmentation procedure. Our preliminary
investigations on the Fluo-N2DH-SIM+ and Fluo-N2DH-GOWT1 datasets demonstrate
that absolute improvements of up to 3.8 % and 3.4% can be obtained in
segmentation and tracking accuracy, respectively.
