Accurate detection and complete tracking of large populations of features in three dimensions
Bibliographic record
Abstract
Localization and tracking of colloidal particles in microscopy images generates the raw data necessary to understand both the dynamics and the mechanical properties of colloidal model systems. Yet, despite the obvious importance of analyzing particle movement in three dimensions (3D), accurate sub-pixel localization of the particles in 3D has received little attention so far. Tracking has been limited by the choice of whether to track all particles in a low-density system, or whether to neglect the most mobile fraction of particles in a dense system. Moreover, assertions are frequently made on the accuracies of methods for locating particles in colloid physics and in biology, and the field of particle locating and tracking can be well-served by quantitative comparison of relative performances. We show that by iterating sub-pixel localization in three dimensions, the centers of particles can be more accurately located in three-dimensions (3D) than with all previous methods by at least half an order of magnitude. In addition, we show that implementing a multi-pass deflation approach, greater fidelity can be achieved in reconstruction of trajectories, once particle positions are known. In general, all future work must defend the accuracy of the particle tracks to be considered reliable. Specifically, other researchers must use the methods presented here (or an alternative whose accuracy can be substantianted) in order for the entire investigation to be considered legitimate, if the basis of the physical argument (in colloids, biology, or any other application) depends on quantitative accuracy of particle positions. We compare our algorithms to other recent and related advances in location/tracking in colloids and in biology, and discuss the relative strengths and weaknesses of all the algorithms in various situations. We carry out performance tests directly comparing the accuracy of our and other 3D methods with simulated data for both location and tracking, and in providing relative performance data, we assess just how accurately software can locate particles. We discuss how our methods, now applied to colloids, could improve the location and tracking of features such as quantum dots in cells.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".