Po‐Poster ‐ 25: Robust 3D prostate model reconstruction from a sparse collection of non‐parallel 2D TRUS biopsy images
Bibliographic record
Abstract
Biopsy of the prostate using 2D transrectal ultrasound (TRUS) guidance is the current gold standard for diagnosis of prostate cancer. Physicians' procedural accuracy and precision is limited by working within the current 2D biopsy environment that is susceptible to uncertainties when targeting 3D biopsy locations. We propose a technique for the reconstruction of a patient‐specific 3D prostate volume from a sparse collection of 2D US biopsy images that may be utilized for accurate prostate volume calculation. 2D TRUS biopsy images, with known 3D coordinates, were simulated from 3D US prostate image volumes acquired from biopsy patients. The prostate boundaries were manually outlined from each simulated biopsy image and radial basis functions were used to estimate the 3D prostate capsule from collections of 2D prostate outlines varying from 6–16 contours. Each reconstructed prostate model was compared to a manually segmented, 3D planimetry model for volume and surface boundary accuracy as well as the clinical‐standard prolate ellipsoid volume estimation technique. Prostate models reconstructed from one patient's simulated biopsy images demonstrated a consistent volume error range of 1.1%–0.4%, which was less than the clinical standard calculation that produced a 1.44% error. The mean prostate surface boundary error for all generated models was consistently < 1 mm with a RMS ≅ 1.1 mm. We have demonstrated a reconstruction technique capable of generating a 3D prostate model from a small sample of 2D TRUS biopsy images. This reconstruction technique has the potential to be incorporated into biopsy protocol for accurate volume measurements and needle guidance.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".