Ventilation Segmentation Accuracy Enhancement using Kirsch Operator in Pulmonary MRI
Bibliographic record
Abstract
Hyperpolarized (HP) noble gas magnetic resonance imaging (MRI) and inert fluorinated gas MRI are the main pulmonary functional imaging modalities capable of regional ventilation assessment. To quantify pulmonary ventilation, K-means-based segmentation of ventilation images is usually performed. K-means clustering, however, performs poorly on images with relatively low SNR and cannot effectively distinguish background noise from low-ventilated areas. Here, we demonstrate a novel ventilation segmentation approach based on the subsequent implementation of lung edge detection using the Kirsch Compass operator followed by K-means segmentation. Our segmentation algorithm resulted in significantly higher accuracy of ventilation defect volume (VDV) assessment compared to a conventional ventilation segmentation algorithm. There were no significant changes in ventilation volume (VV) between our segmentation approach and conventional K-means segmentation. These indicate that implementation of digital lung edge detection prior to K-means segmentation substantially decreases segmentation sensitivity to background noise and allows a much more accurate assessment of low-ventilated regions of the lungs.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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 | 0.000 |
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".