Semi-automatic segmentation of prostate by directional search for edge boundaries
Bibliographic record
Abstract
Semi-automatic segmentation of the prostate boundary is presented for the pre-operational images of the MRIguided \nultrasonic thermal therapy of the prostate cancer. The specific deformable surface method is based on \nfirstly fitting an ellipsoid on the given manual landmark points, then modifying the shape of the initialization \nsurface mesh by masking out the regions of the separately segmented bladder and rectum, and finally adapting \nthe surface mesh by searching image for the edge boundaries in the direction of the surface normal. The \nsuggested segmentation method combines information from two types of pre-operational MR-images showing \ndifferent contrast for the tissue structure. Dice similarity coefficient (DSC) between the semi-automatic \nsegmentation and the manual reference was on average 0.89 for a group of N=5 patients having the MRI guided \nultrasound thermal treatment. The robustness of the surface fitting method was tested by simulating 30 \nrandomized initialization sets of the landmark points for each patient, and the resulting standard deviation of \nDSC was 0.01.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".