Poster — Thur Eve — 05: Semi‐Automated Segmentation of Lung Tumours on CT Scans Using Level Set Sparse Field Active Model
Bibliographic record
Abstract
We present a semi‐automated algorithm for segmenting lung tumours on chest computed tomography (CT) images to be utilized for monitoring tumour response or progression. Seven lung tumours were evaluated; each tumour was radially sliced into 10 slices for a total of 70 radial slices, and manually segmented. The manual segmentations were used as the ground truth for evaluating the proposed algorithm. The tumour image on each radial slice was classified into two categories: (1) well‐defined boundaries, located centrally in the lung parenchyma without significant vasculature; and (2) vascularized or juxtapleural (VJ). To segment the well‐defined boundaries, a shape constrained multi‐thresholding technique with one user‐defined seed point on the tumour is applied. For vascularized and juxtapleural tumours, this multi‐thresholding technique provided an initial contour that was deformed by a level set sparse field active model to produce the final segmentation for these tumours. The dice index (DI) measure was adopted to evaluate the segmentation results. The average and standard deviation values of the DI for the well‐defined and VJ images were 96.32 ± 1.12% and 95.19 ± 1.58%, respectively. The DI overall average and standard deviation of the 70 slices was 95.50 ± 1.55%. The preliminary results show that using the proposed algorithm produced accurate results.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".