Adaptive correction technique for 3D reconstruction of fluorescence microscopy images
Bibliographic record
Abstract
Recent advances in high-resolution imaging have provided valuable novel insights into structural relationships within cells and tissues both in vitro and in vivo. An analysis of this kind is regularly done by optical sectioning using either confocal or deconvolution microscopy. However, the reconstruction of 3D images suffers from light scattering and absorption with increasing depth by finite transparency of the used media. Photobleaching of fluorochromes has been especially troublesome and often the only remedy for loss of signal during optical sectioning is to reduce the number of sections. This causes disparities in the x-y and z dimensions of voxels, which lead to vertical distortion of the original stack of images and necessitates interpolation. Interpolation is necessary to fill up the gaps between consecutive sections in the original image stack to obtain cubic voxels. The present manuscript describes a novel method for adaptive compensation of attenuation of light intensity in stacks of fluorescence microscopy images that is based on a physical model of light attenuation. First, we use a fast interpolation technique to generate a cubic voxel-based volume stack with the aid of a contribution look up table. With the contribution look up table, multiple calculations are avoided, which substantially reduces the computational time without compromising the accuracy of the restoration procedure. Second, each section within the resulting volume is processed to rectify its intensity values that have been altered due to photobleaching and scattering and absorption. The method allows to define the last good section in the stack and the correction is then done automatically.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".