Tracking-Assisted Segmentation of Biological Cells
This work addresses challenges in cell analysis for biomedical research, but it is incremental as it builds on existing U-Net methods.
The paper tackles the problem of biological cell segmentation and tracking in complex scenarios like collisions and mitosis by augmenting U-Net with Siamese matching-based tracking, achieving absolute improvements of up to 3.8% in segmentation and 3.4% in tracking accuracy on specific datasets.
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.