Journal
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
Volume 18, Issue 3, Pages 1060-1069Publisher
IEEE COMPUTER SOC
DOI: 10.1109/TCBB.2019.2936851
Keywords
Image sequences; Feature extraction; Microscopy; Image segmentation; Image edge detection; Cells (biology); Topology; Cell tracking; CNN-based similarity learning; Local graph matching; DeepSeed
Categories
Funding
- National Natural Science Foundation of China [61771189]
- Hunan Provincial Natural Science Foundation of China [2018JJ2060]
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The DeepSeed local graph matching model combines local graph matching and CNN-based similarity learning to robustly find seed cell pairs. Experimental results demonstrate that this method can track most cells in unregistered image sequences and accurately track cells across image sequences with large time intervals.
The tracking of densely packed plant cells across microscopy image sequences is very challenging, because their appearance change greatly over time. A local graph matching algorithm was proposed to track such cells by exploiting the tight spatial topology of neighboring cells, and then an iterative searching strategy was used to grow the correspondence from a seed cell pair. Thus, the performance of the existing tracking approach heavily relies on the robustness of finding seed cell pair. However, the existing local graph matching algorithm cannot guarantee the correctness of the seed cell pair, especially in unregistered image sequences or image sequences with large time intervals. In this paper, we propose a DeepSeed local graph matching model to find seed cell pair robustly, by combining local graph matching and CNN-based similarity learning, which uses cells' spatial-temporal contextual information and cell pairs' similarity information. The CNN-based similarity learning is designed to learn cells' deep feature and measure cell pairs' similarity. Compared with the existing plant cell matching methods, the experimental results show that the DeepSeed local graph matching method can track most cells in unregistered image sequences. Moreover, the DeepSeed tracking algorithm can accurately track cells across image sequences with large time intervals.
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