Targeting error simulator for image-guided prostate needle placement
Bibliographic record
Abstract
MOTIVATION: Needle-based biopsy and local therapy of prostate cancer depend multimodal imaging for both target planning and needle guidance. The clinical process involves selection of target locations in a pre-operative image volume and registering these to an intra-operative volume. Registration inaccuracies inevitably lead to targeting error, a major clinical concern. The analysis of targeting error requires a large number of images with known ground truth, which has been infeasible even for the largest research centers. METHODS: We propose to generate realistic prostate imaging data in a controllable way, with known ground truth, by simulation of prostate size, shape, motion and deformation typically encountered in prostatic needle placement. This data is then used to evaluate a given registration algorithm, by testing its ability to reproduce ground truth contours, motions and deformations. The method builds on statistical shape atlas to generate large number of realistic prostate shapes and finite element modeling to generate high-fidelity deformations, while segmentation error is simulated by warping the ground truth data in specific prostate regions. Expected target registration error (TRE) is computed as a vector field. RESULTS: The simulator was configured to evaluate the TRE when using a surface-based rigid registration algorithm in a typical prostate biopsy targeting scenario. Simulator parameters, such as segmentation error and deformation, were determined by measurements in clinical images. Turnaround time for the full simulation of one test case was below 3 minutes. The simulator is customizable for testing, comparing, optimizing segmentation and registration methods and is independent of the imaging modalities used.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".