Markerless mouse tracking for social experiments
Bibliographic record
Abstract
Abstract Automated behavior quantification requires accurate tracking of animals. Simultaneous tracking of multiple animals, particularly those lacking visual identifiers, is particularly challenging. Problems of mistaken identities and lost information on key anatomical features are common in existing methods. Here we propose a markerless video-based tool to simultaneously track two socially interacting mice of the same appearance. It incorporates conventional handcrafted tracking and deep learning based techniques, which are trained on a small number of labeled images from a very basic, uncluttered experimental setup. The output consists of body masks and coordinates of the snout and tail-base for each mouse. The method was tested on a series of cross-setup videos recorded under commonly used experimental conditions including bedding in the cage and fiberoptic or headstage implants on the mice. Results obtained without any human intervention showed the effectiveness of the proposed approach, evidenced by a near elimination of identities switches and a 10% improvement in tracking accuracy over a pure deep-learning-based keypoint tracking approach trained on the same data. Finally, we demonstrated an application of this approach in studies of social behaviour of mice, by using it to quantify and compare interactions between pairs of mice in which some are anosmic, i.e. unable to smell. Our results indicated loss of olfaction impaired typical snout-directed social recognition behaviors of mice, while non-snout-directed social behaviours were enhanced. Together, these results suggest that the hybrid approach could be valuable for studying group behaviors in rodents, such as social interactions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".