Automated Segmentation of the Cerebral Ventricles on CT Images
Bibliographic record
Abstract
Accurate assessment of the volume of cerebral ventricles on computed tomographic (CT) images of the brain is an important and as yet unsolved problem in neuroradiology. Subtle changes in ventricular volume occur early in the development or progression of hydrocephalus, a potentially life-threatening condition that may require urgent surgical treatment. Current subjective assessment of ventricles by neuroradiologists and neurosurgeons has limited accuracy, because of the complex shape of the ventricular system. Comparison of ventricles as depicted on serial imaging studies of the same patient are confounded by differences in the angulations of slices from one study to the next. We are developing an automated system that can segment the cerebral ventricles on axial computed tomographic images of the brain. Two automated segmentation techniques have been developed and tested. One is based on thresholding and the other on region growing. The results have been compared to a manual segmentation by calculating the similarity index (S). A total of ten cases, each with approximately 20 slices, were tested and a good result (S>0.7) was obtained.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".