Three-dimensional ultrasound-guided minimally invasive therapy of the prostate
Bibliographic record
Abstract
Prostate cancer is the most commonly diagnosed cancer in men in North America. Although 2D transrectal ultrasound imaging is widely used for the evaluation of prostate disease it suffers from limitations that limit its use in diagnosis and therapy of prostate cancer. The outcome of a 2D-ultrasound guided minimally invasive procedure depends on the skill and expertise of the operator in manipulating the transducer and in forming a correct mental impression of the 3D anatomy and pathology. In addition, the process of quantifying and monitoring small changes during the therapeutic procedure is also severely limited by using a spatially variable 2D imaging technique. We have developed a 3D ultrasound imaging approach that overcomes these problems. In this paper, we describe a 3D ultrasound imaging system for use in prostate imaging and report on its performance. The system consists of a conventional ultrasound machine, a microcomputer with an video frame grabber, and a custom-built assembly for rotating the ultrasound transducer. A typical scan of 100 2D B-mode images takes 7 seconds. These images can then be reconstructed into a 3D image, which can be displayed and interactively manipulated using 3D visualization software. We also show that the process of reconstruction does not distort the geometry, and that the 3D system can be used to localize brachytherapy seeds in phantoms with a precision (SEM) of better than 0.4 mm. We also show that the 3D system can be used to image brachytherapy seeds in patients post-implantation.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".