Detection and tracking of low contrast human sperm tail
Bibliographic record
Abstract
Tracking sperm tail movement provides important information for clinical sperm research. It is also a crucial step for sperm immobilization in Intracytoplasmic Sperm Injection (ICSI). However, the low visibility of the sperm tail under optical microscopy, coupled with the sperm fast motility, render sperm tail identification and tracking challenging tasks to execute. This paper presents two approaches for sperm tail tracking: (1) the Maximum Intensity Region (MIR) algorithm, and (2) the Optical Flow (OF) algorithm. The algorithms were evaluated by calculating the Euclidean distance error between each tail tracking algorithm's computed tail location and a user's manual input via mouse click of the tail's image location. Experimental results demonstrate that the OF algorithm and MIR algorithm are both capable of tracking the sperm tail with minimal error when viscous liquid is added to the sperm culture medium, which is the present clinical standard practice for slowing down sperm movement. The MIR algorithm outperforms the OF algorithm by 52% in tail tracking accuracy in situations where the viscous liquid is absent.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".