A multi-steps segmentation approach for 3D ultrasound images using the combination of 3D-Snake and Level-Set
Bibliographic record
Abstract
The segmentation task of 3D ultrasound images has been investigated by a lot of researchers, due to the advantage that it provides to non-invasively detect internal organs and medical conditions. The challenging problems toward the segmentation of 3D ultrasound images are the presence of high speckle noise, inconsistent intensity level and gaps within walls. We propose a new idea by combining 3D-Snake and Level-Set approaches to overcome ultrasound difficulties and maintain high accuracy to segment bleeding regions. The proposed approach starts with a denosing step. The 3D-Snake is resistive to boundaries' discontinuities, and therefore is applied to robustly extract the approximation of the volume of interest. Then, the Level-Set approach is utilized to increase the segmentation accuracy in a few iterations. The parameters of the proposed method are analysed and its segmentation accuracy is evaluated and compared with the Level-Set method. The obtained results validate the superiority of the proposed method.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".