An Automated Approach for Kidney Segmentation in Three-Dimensional Ultrasound Images
Bibliographic record
Abstract
Automated segmentation of kidneys in three-dimensional (3-D) abdominal ultrasound volumes is a task of paramount importance in automated diagnosis of abdominal trauma. However, ultrasound speckle noise, low-contrast boundaries, partial kidney occlusion, and probe misalignment restrict the utility of the solution, especially when it is used in emergency rooms and Focused Assessment with Sonography Trauma applications. This paper introduces a systematic and cost-effective method capable of detecting and segmenting the kidney's shape in acquired 3-D ultrasound volumes, using off-line training datasets. This paper offers a new shape model representation, called the complex-valued implicit shape model, to generate a 3-D kidney shape model by combining prior knowledge of training shapes and anatomical knowledge. We apply shape-to-volume registration, based on a new similarity metric, to detect the kidney shape by fitting the 3-D shape model on 3-D ultrasound volumes. Upon kidney detection, the fitted shape model is used to initialize and evolve a new level-set function, called complex-valued rational level-set with shape prior, to segment the kidney's shape. Experimentation using both simulated and actual ultrasound volumes indicate that the proposed solution provides a better performance over the state-of-the-art volumetric ultrasound segmentation methods.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".