Correction of motion artifacts in endoscopic optical coherence tomography and autofluorescence images based on azimuthal en face image registration
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Bibliographic record
Abstract
We present a method for the correction of motion artifacts present in two- and three-dimensional in vivo endoscopic images produced by rotary-pullback catheters. This method can correct for cardiac/breathing-based motion artifacts and catheter-based motion artifacts such as nonuniform rotational distortion (NURD). This method assumes that en face tissue imaging contains slowly varying structures that are roughly parallel to the pullback axis. The method reduces motion artifacts using a dynamic time warping solution through a cost matrix that measures similarities between adjacent frames in en face images. We optimize and demonstrate the suitability of this method using a real and simulated NURD phantom and in vivo endoscopic pulmonary optical coherence tomography and autofluorescence images. Qualitative and quantitative evaluations of the method show an enhancement of the image quality.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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 it