Single-click, semi-automatic lung nodule contouring using hierarchical conditional random fields
Bibliographic record
Abstract
Lung cancer is the second most common cancer in the United States, regardless of gender. Lung cancer staging is a critical process for diagnosis and prognosis that is commonly done through the analysis of computed tomography of the chest. Analysis can be done by extracting quantitative metrics from clinician defined contours; however, defining contours manually can be a time consuming process which, in an environment where fast staging is necessary, is undesirable. Semi-automatic methods are desirable for minimizing user input while achieving similar contouring results. However, they can be hampered by image noise and complex shapes. In this paper, we present a single-click, semi-automatic contouring method for lung nodules that formulates the problem as a texture-based hierarchical conditional random field, making it robust to noise and capable of contouring complex shapes. Comparing against other semi-automatic contouring methods using clinician contours as ground truth, the proposed method achieves higher results in the sensitivity, Dice, and Jaccard metrics while achieving comparable results with the specificity and accuracy metrics.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 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".