ZebraTrack: An Open-Source Object Detection Algorithm to Detect and Track Larval Zebrafish Motor Touch Responses
Bibliographic record
Abstract
Abstract Motivation Zebrafish ( Danio rerio ) are a model organism used for the study of vertebrate development, disease and drug discovery. Two-day old larval zebrafish exhibit burst swimming behaviour that can be elicited by a light touch to the tail. Larval motor touch-responses are frequently video recorded and later analyzed. Methods to robustly analyze these videos in a reproducible and time-efficient manner are reliant on manual tracking, which is prone to experimenter bias and error. Results Here we present ZebraTrack, a machine learning-based program, which employs Ultralytics’ YOLOv8 nano (YOLOv8n) object detection algorithm to automatically analyze larval touch response videos. The program breaks down video files into their constituent frames and passes these through a custom-trained YOLOv8n algorithm to detect the presence of a single larval zebrafish. ZebraTrack then refines the tracking data output by the model and tabulates it into an excel spreadsheet. The program then computes and extracts four relevant swim metrics: swim duration (s), swim distance (mm), mean swim velocity (mm/s), and max swim velocity (mm/s). ZebraTrack rapidly accelerates the analysis process, while also eliminating the errors associated with manual tracking. Furthermore, it allows for high-throughput analysis of larval touch response videos and can detect subtle differences in motor metrics arising as a result of temperature differences, demonstrating that utility of this tracking algorithm. Availability and implementation ZebraTrack is available for download at https://github.com/Armstrong-Lab-70/ZebraTrack .
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".