Estimation of Lung's Air Volume and Its Variations Throughout Respiratory CT Image Sequences
Bibliographic record
Abstract
A respiratory image-sequence-segmentation technique is introduced based on a novel image-sequence analysis. The proposed technique is capable of segmenting the lung's air and its soft tissues followed by estimating the lung's air volume and its variations throughout the image sequence. Accurate estimation of these two parameters is very important in many applications related to lung disease diagnosis and treatment systems (e.g., brachytherapy), where the parameters are either the variables of interest themselves or are dependent/independent variables. The concept of the proposed technique involves using the image sequence's combined histogram to obtain a reasonable initial guess for the lung's air segmentation thresholds. This is followed by an optimization process to find the optimum threshold values that best satisfy the lung's air mass conservation and tissue incompressibility principles. These threshold values are consequently applied to estimate the lung's air volume and its variations throughout respiratory Computed Tomography (CT) image sequences. Ex vivo experiments were conducted on porcine left lungs in order to demonstrate the performance of the proposed technique. The proposed method was initially validated using a breath-hold CT image sequence with known air volumes inside the lung, where results show that the proposed technique outperforms single-histogram-based methods. This was followed by demonstrating the proposed technique's application in a 4-D-CT respiratory sequence, where the air volume inside the lung was unknown. Consistency of the obtained results in the latter experiment with tissue near incompressibility principle was validated. The results indicate a very good ability of the proposed method for estimating the lung's air volume and its variations in a respiratory image sequence.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".