Segmentation of Lung Lobes in Volumetric CT images for Surgical Planning of Treating Lung Cancer
Bibliographic record
Abstract
Study has shown that three-dimensional (3D) visualization of lung cavities has distinct advantages over traditional computed tomographic (CT) images for surgical planning. A crucial step for achieving 3D visualization of lung cavities is the segmentation of lung lobes by identifying lobar fissures in volumetric CT images. Current segmentation algorithms for lung lobes rely on manually placed markers to identify the fissures. This paper presents an autonomous algorithm that effectively segments the lung lobes without user intervention. This algorithm applies a two-stage approach: (a) adaptive fissure sweeping to coarsely define fissure regions of lobar fissures; and (b) watershed transform to refine the location and curvature of fissures within the fissure regions. We have tested this algorithm on 4 CT data sets. Comparing with visual inspection, the algorithm provides an accuracy of 85.5-95.0% and 88.2-92.3% for lobar fissures in the left and right lungs, respectively. This work proves the feasibility of developing an automatic algorithm for segmenting lung lobes
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".