Markerless Mouse Tracking for Social Experiments
Bibliographic record
Abstract
Automated behavior quantification in socially interacting animals requires accurate tracking. While many methods have been very successful and highly generalizable to different settings, issues of mistaken identities and lost information on key anatomical features are common, although they can be alleviated by increased human effort in training or post-processing. We propose a markerless video-based tool to simultaneously track two interacting mice of the same appearance in controlled settings for quantifying behaviors such as different types of sniffing, touching, and locomotion to improve tracking accuracy under these settings without increased human effort. It incorporates conventional handcrafted tracking and deep-learning-based techniques. The tool is trained on a small number of manually annotated images from a basic experimental setup and outputs body masks and coordinates of the snout and tail-base for each mouse. The method was tested on several commonly used experimental conditions including bedding in the cage and fiberoptic or headstage implants on the mice. Results obtained without any human corrections after the automated analysis showed a near elimination of identities switches and a ∼15% improvement in tracking accuracy over pure deep-learning-based pose estimation tracking approaches. Our approach can be optionally ensembled with such techniques for further improvement. Finally, we demonstrated an application of this approach in studies of social behavior of mice by quantifying and comparing interactions between pairs of mice in which some lack olfaction. Together, these results suggest that our approach could be valuable for studying group behaviors in rodents, such as social interactions.
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How this classification was reachedexpand
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".