Processing, visualization and analysis of 3D confocal microscopic images
Bibliographic record
Abstract
Confocal laser scanning microscopes have been widely employed in biology research for many years.The advancement of computer technology enables interactive visualization and quantitative analysis of volumetric confocal image datasets thus giving deep insights into cellular structures.This thesis details the computer techniques developed by the author to facilitate 3D confocal image studies.The key contributions of this work are in (1) confocal image acquisition and management, (2) 3D image processing, and (3) Virtual Reality (VR) enhanced visualization and analysis of cellular structures.With a large amount of confocal images produced with microscopes for different biological studies, the administration of these image datasets become crucial.We develop a solution enabling convenient storage and management of confocal image data with a central image administration database.Furthermore, the integration of Internet techniques with the database makes it possible for online data access in a collaborative research environment.Raw confocal image data has various problems thus usually needs to be processed before visualization and quantitative analysis.A fast interpolation algorithm to produce
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.007 | 0.004 |
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".