3D prostate segmentation based on ellipsoid fitting, image tapering and warping
Bibliographic record
Abstract
This paper presents a 3D semi-automatic prostate segmentation method for B-mode trans-rectal ultrasound (TRUS) images. Segmentation is based on prior knowledge of the prostate transversal section shape which is assumed to be a tapered ellipse. The approach consists of an initial untapering and warping of the image to make the shape of the prostate approximately elliptical. The prostate contour is found in the untapered, warped images by edge detection and 2D/3D ellipsoidal curve fitting, performed by solving convex optimization problem. This leads to an average total segmentation duration of 7.42s which is more than 40 times faster than manual segmentation. Clinical studies carried out on 58 prostate volume studies show a volume sensitivity and accuracy of approximately 94% and 73% respectively.
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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 | 0.001 |
| 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".