Segmentation of Prostate from 3-D Ultrasound Volumes Using Shape and Intensity Priors in Level Set Framework
Bibliographic record
Abstract
This paper presents a fully automatic prostate segmentation system in transrectal ultrasound images based on 3-D shape and intensity priors. 2-D manual segmentations from training image data are stacked to create the coarse 3-D shape. Min/Max flow is used to transform each coarse shape into smooth 3-D surface. Principle component analysis method is utilized to extract the 3-D shape mode from the training data sets. In a Bayesian inference, the nonlinear shape model is integrated with a nonparametric intensity prior and define a region based energy function. The energy is minimized in a level set frameworks and the control parameters of the convergence lead to the final segmentation. The developed method was tested on 3-D transrectal ultrasound images and its performance compared with manually-defined ground truth. The correct segmentation rate is 0.82
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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".