Sci‐AM1 Sat ‐ 02: Dynamic intraoperative prostate brachytherapy
Bibliographic record
Abstract
The aim of this paper is to develop a dynamic intraoperative prostate brachytherapy system in order to deal with variabilities in the current procedure. System consists of 3D TRUS imaging, a robot and software for prostate segmentation, intraoperative planning, oblique needle segmentation and tracking, seed segmentation, and 3D dose planning. The robot and 3D TRUS coordinate systems are unified with robot and image calibrations. In 3D TRUS images, the prostate is segmented using the discrete dynamic contour method, and optimal implantation plan is performed by using geometric optimization followed by simulated annealing. The inserted needles are segmented and tracked using grey‐level change in near real‐time, and seed segmentation is performed using 3D line segment patterns. Needle placement accuracy of the robot at the “patient” skin was 0.15mm±0.06mm, and needle angulation error was 0.07°. Needle targeting accuracy was 0.79mm±0.32mm. The average difference between manual and the prostate segmentation algorithm of prostate boundaries was −0.20±0.28mm. In our needle tracking tests, errors in determining needle orientation were less than 2° in robot yaw and 0.7° in robot pitch orientations, for up to 20° needle insertion angles when the needle insertion distance was greater than 15mm. The true‐positive rates for the seed segmentation algorithm in 3D TRUS images were 100% for agar and 93% for chicken phantoms. The result of this work provides a tool to achieve dynamic intraoperative prostate brachytherapy using 3D TRUS imaging and robotic assistance together with efficient segmentation software.
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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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".