Slice-Based Prostate Segmentation in 3D US Images Using Continuity Constraint
Bibliographic record
Abstract
In the diagnosis and therapy of prostate cancer, it is critical to measure the volume of the prostate and locate its boundary. Three-dimensional transrectal ultrasound (3D TRUS) imaging has been demonstrated to be a useful technique to perform such a task. Due to image speckle as well as low contrast in ultrasound images, segmentation of the prostate in 3D US images is challenging. In this paper, we report on the development of an improved slice-based 3D prostate segmentation method. First, we imposed a continuity constraint for the end points of the prostate boundaries in a cross-sectional plane so that a smooth prostate boundary in 2D is obtained. Then, in each 2D slice, we inserted the end points into the vertex list of the initial contour to obtain a new contour, which forces the evolving contour to be driven to the boundary of the prostate. Evaluation demonstrated that our method could segment the prostate in 3D TRUS images more quickly and accurately.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".