Cell Tracking in Video Microscopy Using Bipartite Graph Matching
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Bibliographic record
Abstract
Automated visual tracking of cells from video microscopy has many important biomedical applications. In this paper, we model the problem of cell tracking over pairs of video microscopy image frames as a minimum weight matching problem in bipartite graphs. The bipartite matching essentially establishes one-to-one correspondences between the cells in different frames. A key advantage of using bipartite matching is the inherent scalability, which arises from its polynomial time-complexity. We propose two different tracking methods based on bipartite graph matching and properties of Gaussian distributions. In both the methods, i) the centers of the cells appearing in two frames are treated as vertices of a bipartite graph and ii) the weight matrix contains information about distance between the cells (in two frames) and cell velocity. In the first method, we identify fast-moving cells based on distance and filter them out using Gaussian distributions before the matching is applied. In the second method, we remove false matches using Gaussian distributions after the bipartite graph matching is employed. Experimental results indicate that both the methods are promising while the second method has higher accuracy.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it