Semiautomatic Lymph Node Segmentation in Multislice Computed Tomography
Bibliographic record
Abstract
OBJECTIVES: To determine the impact of slice thickness on semiautomatic lymph node analysis. MATERIALS AND METHODS: Thoracic multislice computed tomography (MSCT) of 46 patients with NSCLC were reconstructed at 1.0-, 3.0-, and 5.0-mm slice thickness. Two radiologists independently determined long and short axis diameter and volume of 299 thoracic lymph nodes by semiautomatic segmentation software. Necessity of manual correction (= relative difference between uncorrected and corrected segmented lymph node volume) and relative interobserver differences were determined. The precision of segmentation was expressed by relative measurement deviations (RMD) from the reference standard (mean of 1.0 mm datasets). Statistical analysis encompassed t test and Bland-Altman plots. RESULTS: Necessity of manual correction was significantly higher for 5.0 mm than for 3.0 (P = 0.042) or 1.0 mm (P = 0.0012). The RMD for long and short axis diameter were found to be independent of slice thickness, whereas the RMD for lymph node volume significantly (P = 0.021) increased from 4.0% at 1.0 mm (95% CI: 1.0%-3.5%) to 35% at 5.0 mm (95% CI: 10.5%-60.5%). The relative interobserver differences was consistently low for metric and volumetric parameters (eg, volume 2.3%, 95% CI: -7.4%-10.8% at 5.0 mm) with no difference in any of the slice thicknesses (P > 0.064). CONCLUSIONS: Significant deviations in lymph node volume together with excessive manual corrections suggest reconstruction of the data for semiautomatic lymph node assessment at a slice thickness of 1.0 mm but not exceeding 3.0 mm.
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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".