Cell Image Analysis Technology Applications
Neural Stem Cell Tracking
| Automated tracking of individual cells in populations aims at obtaining fine-grained measurements of cell behaviors, including migration (translocation), mitosis (division), apoptosis (death), shape deformation of individual cells, and their interactions among cells. Such detailed analysis of cell behaviors requires the capabilities to reliably track cells that may sometimes partially overlap, forming cell clusters, and to distinguish cellular mitosis/fusion from split and merge of cell clusters. Existing cell tracking algorithms are short of these capabilities. We propose a cell tracking method based on partial contour matching that is capable of robustly tracking partially overlapping cells, while maintaining the identity information of individual cells throughout the process from their initial contact to eventual separation. The method has been applied to a task of tracking human central nervous system (CNS) stem cells in differential interference contrast (DIC) microscopy image sequences, and has achieved 97% tracking accuracy. | ||
Example 1. Tracking results of the proposed method used when two cells overlap. |
Example 2. Tracking results of the proposed method used when three cells overlap. |
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Neural stem cells often touch or overlap. When cells touch or overlap, existing algorithms either lose track of one or more cells or confuse their identities.
We propose a cell tracking method based on partial contour matching that is capable of robustly tracking partially overlapping cells. |
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Tracking results |
Segmenting a cluster into members |
Detected cell blob region overlaid on the original image |