A MULTI-SEED 3D LOCAL GRAPH MATCHING MODEL FOR TRACKING OF DENSELY PACKED CELLS

Automated tracking of cells in time-lapse live-imaging datasets of developing multicellular tissues is required for high throughput spatio-temporal quantitative measurements of a range of cell behaviors. The tracking of shoot apical meristems (SAM) cells in large-scale microscopy image sequences is challenging, because plant cells are densely packed within a specific honeycomb structure and share very similar physical features. In this paper, we propose a 3D local graph matching model to track the plant SAM cells, by exploiting the cells' tight spatial and temporal contextual information. The proposed 3D local graph matching model is further combined with a multi-seed based majority voting scheme to rectify possible matching errors in the cell correspondence growing process. Compared with the existing 2D local graph matching model, the experimental results show that the proposed method can greatly improve the tracking accuracy for plant cells.