Optical Computed‐Tomographic Microscope for Three‐Dimensional Quantitative Histology
Bibliographic record
Abstract
A novel optical computed-tomographic microscope has been developed allowing quantitative three-dimensional (3D) imaging and analysis of fixed pathological material. Rather than a conventional two-dimensional (2D) image, the instrument produces a 3D representation of fixed absorption-stained material, from which quantitative histopathological features can be measured more accurately. The accurate quantification of these features is critically important in disease diagnosis and the clinical classification of cancer. The system consists of two high NA objective lenses, a light source, a digital spatial light modulator (DMD, by Texas Instrument), an x-y stage, and a CCD detector. The DMD, positioned at the back pupil-plane of the illumination objective, is employed to illuminate the specimen with parallel rays at any desired angle. The system uses a modification of the convolution backprojection algorithm for reconstruction. In contrast to fluorescent images acquired by a confocal microscope, this instrument produces 3D images of absorption stained material. Microscopic 3D volume reconstructions of absorption-stained cells have been demonstrated. Reconstructed 3D images of individual cells and tissue can be cut virtually with the distance between the axial slices less than 0.5 microm.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".