Registration of whole-mount histology and tomography of the prostate using particle filtering
Bibliographic record
Abstract
Registration of histological slices to volumetric imaging of the prostate is an important task that can be used to optimize imaging for cancer detection. Such registration is challenging due to change in volume of the specimen during fixation, and misalignment of the histological slices during preparation and digital scanning. In this work we propose a multiple-slice to volume registration method in which a stack of equispaced, uniaxial but unaligned 2D contours, extracted from digitally scanned whole-mount histological slices, is registered to a 3D surface, extracted from a volumetric image of the prostate. Initially, the stack of unaligned contours is coarsely aligned to the surface as a whole. Then, each contour is finely registered to the surface while being confined to its plane along the sectioning axis. We incorporate the method in a particle filtering framework to compensate for the high dimensionality of the search space and multi-modal nature of the problem. Moreover, such framework allows modeling the uncertainty in the segmentation of the contours and surface, in order to derive optimal registration parameters in a Bayesian approach. The proposed algorithm is demonstrated and evaluated on both synthetic and clinical data. The mean area overlap of the registered gland and the segmented histology was found to be 90.2%, with a mean registration error of 1.8mm between visible landmarks.
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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) | 0.001 | 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) | 0.001 | 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 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".