MO‐E‐330A‐02: Automated Prostate Contour Drawing On Post‐Implant CT Images Based On Ultrasound Volume and Seeds Positions
Bibliographic record
Abstract
Purpose: It is difficult to locate precisely the prostate on CT images. We propose an automated contouring help method based on data acquired during intra‐operative brachytherapy procedure. The algorithm uses ultrasound volume and seeds positions to draw a preliminary contour on CT image. Method and Materials: The data acquired during the clinical protocol are the intra‐operative ultrasound volume and the seeds positions based on the detected needle insertion, and the seed positions and the CT images thirty days post‐implant. In the first step the US volume and seed cloud are matched. For each z position of a clinical CT image, the US contour and seeds are extracted. The seeds positions are automatically detected on CT image and are used to calculate a transformation matrix to translate the seeds positions from day 0 to 30. The contours are reshaped using active contour. The reshaping process begins with the center slice and progress on both sides. The active contours are initialize with an expanded US volume. Implementation (results): There are no parameters to adjust in the first part of the algorithm. In the second part, there are six snakes parameters : continuity, curvature, convergence, gradient contraction minimum and contraction maximum. There is also a parameter controlling the resize factor of the US contours. The preliminary tests are conducted on ten clinical cases. Most of the contours were the final contour. The others needed a small physician action to correct the contours. Conclusion: This algorithm will be a useful tool to help physicians in tedious work to draw prostate contours on CT images. This automated approach presents the physician with intra‐op US volume fused to the 30 days CT exams and proposes a new set of contours based on the morphology of the seed distribution.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 0.000 |
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 teacher head, 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".