Multi-catheter modelling in reconstructed 3D transrectal ultrasound images from prostate brachytherapy
Bibliographic record
Abstract
High-dose-rate brachytherapy is an accepted standard-of-care treatment for prostate cancer. In this procedure, catheters are inserted using three-dimensional (3D) transrectal ultrasound image-guidance. Their positions are manually segmented for treatment planning and delivery. The transverse ultrasound sweep, which is subject to tip and depth error for catheter localization, is a commonly used ultrasound imaging option available for image acquisition. We propose a two-step pipeline that uses a deep-learning network and curve fitting to automatically localize and model catheters in transversely reconstructed 3D ultrasound images. In the first step, a 3D U-Net was trained to automatically segment all catheters in a 3D ultrasound image. Following this step, curve fitting was implemented to detect the shapes of individual catheters using polynomial fitting. Of the 343 catheters (from 20 patients) in the testing data, the pipeline detected 320 (93.29%) with 7 false positives (2.04%) and 13 false negatives (3.79%). The average distance± one standard deviation between the ground truth and predictions for each catheter shaft was 1.9 ± 0.3 mm. The average difference of each catheter tip was 3.0 ± 0.4 mm. The proposed pipeline provides a method for reducing time spent on verification of catheter positions, minimizing uncertainties, and improving clinical workflow during the procedure. Reducing human variability in catheter placement predictions may increase the accuracy of tracking and radiation dose modelling.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".