3D carotid reconstructions: imaging, pathology, algorithms and pipelines
Bibliographic record
Abstract
Background: Whole-slide scanning of tissue sections spatially informed by imaging studies offers the opportunity to reconstruct specimens for co-registration to 3D imaging data. Digital image analysis algorithms can be designed to analyze and reconstruct such specimens via electronic “pipelines”. Methods: A goal of the Canadian Atherosclerosis Imaging Network (CAIN) is to improve the assessment of carotid atheromatous disease through studies that inform clinical imaging with gold-standard data (plaque pathology). To achieve this, sectioned atheromas are manually annotated and analyzed by electronic algorithm for pathological features of interest. Resulting images are then reassembled in 3D for registration to ultrasound, CT, PET-CT and MRI studies. Results: Carotid endarterectomy specimens were sub-serially sectioned, stained, digitized and annotated manually and by electronic algorithms. Resulting 2D images were successfully rendered, reassembled and analyzed in 3D using ex-vivo micro-CT as a spatial reference. Furthermore, histology quantification using colour deconvolution was found to be preferred over hue-saturation-intensity methods 94.7-100% of the time in a blinded multiple rater study. Conclusion: Automated “pipelines” greatly facilitate 3D reconstruction in comparison to traditional slice-by-slice methods. Transformations spatially guided by pre-existing imaging data is not only faster, but has superior objectivity and fidelity. With embedded annotations, 3D pathology maps become a rich, micron-level, permanent digital pathological database for correlative studies.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".