3D prostate histology reconstruction: an evaluation of image-based and fiducial-based algorithms
Bibliographic record
Abstract
Imaging may enable the determination of the spatial distribution and aggressiveness of prostate cancer in vivo before treatment, possibly supporting diagnosis, therapy selection, and focal therapy guidance. 3D reconstruction of prostate histology facilitates the validation of such imaging applications. We evaluated four histology–ex vivo magnetic resonance (MR) image 3D reconstruction algorithms comprising two similarity metrics (mutual information MMI or fiducial registration error MFRE) and two search domains (affine transformations TA or fiducial-constrained affine transformations TF). Seven radical prostatectomy specimens were imaged with MR imaging, processed for whole-mount histology, and digitized as histology images. The algorithms were evaluated on the reconstruction error and the sensitivity of same to translational and rotational errors in initialization. Reconstruction error was quantified as the target registration error (TRE): the post-reconstruction distance between homologous point landmarks (7–15 per histology section; 132 total) identified on histology and MR images. Sensitivity to initialization was quantified using a linear model relating TRE to varied levels of translational/rotational initialization errors. The algorithm using MMI and TA yielded a mean TRE of 1.2±0.7 mm when initialized using an approach that assumes histology corresponds to the front faces of tissue blocks, but was sensitive to initialization error. The algorithm using MFRE and TA yielded a mean TRE of 0.8±0.4 mm with minimal sensitivity to initialization errors. Compared to the method used to initialize the algorithms (mean TRE 1.4±0.7 mm), a study using an algorithm with a mean TRE of 0.8 mm would require 27% fewer subjects for certain imaging validation study designs.
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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 | 0.012 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 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".