Automated Filtering of Intrinsic Movement Artifacts during Two-Photon Intravital Microscopy
Bibliographic record
Abstract
In vivo imaging using two-photon microscopy is an essential tool to explore the dynamic of physiological events deep within biological tissues for short or extended periods of time. The new capabilities offered by this technology (e.g. high tissue penetrance, low toxicity) have opened a whole new era of investigations in modern biomedical research. However, the potential of using this promising technique in tissues of living animals is greatly limited by the intrinsic irregular movements that are caused by cardiac and respiratory cycles and muscular and vascular tone. Here, we show real-time imaging of the brain, spinal cord, sciatic nerve and myenteric plexus of living mice using a new automated program, named Intravital_Microscopy_Toolbox, that removes frames corrupted with motion artifacts from time-lapse videos. Our approach involves generating a dissimilarity score against precalculated reference frames in a specific reference channel, thus allowing the gating of distorted, out-of-focus or translated frames. Since the algorithm detects the uneven peaks of image distortion caused by irregular animal movements, the macro allows a fast and efficient filtering of the image sequence. In addition, extra features have been implemented in the macro, such as XY registration, channel subtraction, extended field of view with maximum intensity projection, noise reduction with average intensity projections, and automated timestamp and scale bar overlay. Thus, the Intravital_Microscopy_Toolbox macro for ImageJ provides convenient tools for biologists who are performing in vivo two-photon imaging in tissues prone to motion artifacts.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".