Robust Plant Cell Tracking in Noisy Image Sequences Using Optimal CRF Graph Matching

In time-lapse live-imaging datasets of developing multicellular tissues, automated tracking of cells is required for high-throughput spatiotemporal quantitative measurements of a range of cell behaviors. This letter proposes a conditional random field (CRF) graph matching method to track plant cells in noisy images by exploiting the tight spatial topology of neighboring cells in a multicellular field as contextual information. The CRF potential of cells dynamically changes during the cell correspondence growing process, because the cells that have been matched already are not included in the calculation of the second-order potential. Therefore, the proposed CRF-based tracker tends to reduce tracking errors, while the previous local graph matching method tends to accumulate errors during the cell correspondence growing process. The CRF graph matching method greatly improves the tracking accuracy in noisy images and enhances the tracking stability because it always matches the most reliable cell pairs with the least CRF potential in the neighboring system. Compared with the previous method, the experimental results show that the proposed method can improve the tracking accuracy rate by 10% in noisy image sequences.