Developing a 3D Segmentation Technique by a Region-Growing Algorithm of CT Scan Lung Images
Bibliographic record
Abstract
Conventional methodologies for lung image segmentation (LIS) encounter challenges posed by anatomical intricacies and intensity fluctuations in computed tomography (CT) scans.This study introduces a precise and effective approach to segmenting lung areas by utilising a region-growing algorithm.Initial steps involve data pre-processing, encompassing intensity regulation, noise reduction, and identification of lung regions.The core segmentation employs a region-growing algorithm; namely, active contours (ACs); with explicit criteria based on homogeneity, intensity values, and spatial connectivity.This iterative algorithm expands connected regions from seed points (SPs) within the identified lung region, ensuring conformity to defined criteria.Refinement of the segmentation occurs through the merging of neighbouring regions exhibiting similar attributes.Evaluation on a dataset of 196 chronic obstructive pulmonary disease (COPD) patients with varying degrees of lung abnormalities demonstrates accurate three-dimensional (3D) segmentation, yielding an average dice similarity coefficient (DSC) of 0.946 ± 0.023.This performance significantly surpasses that of thresholding methods (DSC: 0.826 ± 0.033), indicating a notably enhanced overlap between segmented lung areas and ground truth data.This study contributes a robust and efficient technique to the realm of LIS, facilitating precise 3D LIS in CT scans.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".