A Model-Based Validation Scheme for Organ Segmentation in CT Scan Volumes
Bibliographic record
Abstract
In this study, we propose a novel approach for accurate 3-D organ segmentation in the CT scan volumes. Instead of using the organ's prior information directly in the segmentation process, here we utilize the knowledge of the organ to validate a large number of potential segmentation outcomes that are generated by a generic segmentation process. For this, an organ space is generated based on the principal component analysis approach using which the fidelity of each segment to the organ is measured. We detail applications of the proposed method for the 3-D segmentation of human kidney and liver in computed tomography scan volumes. For evaluation, the public database of the MICCAI's 2007 grand challenge workshop has been incorporated. Implementation results show an average Dice similarity measure of 0.90 for the segmentation of the kidney. For the liver segmentation, the proposed algorithm achieves an average volume overlap error of 8.7 % and an average surface distance of 1.51 mm.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".