Contouring in the optic plane improves the accuracy of computed tomography‐based segmentation of the optic pathway
Bibliographic record
Abstract
Canine optic pathway structures are often contoured on CT images, despite the difficulty of visualizing the optic pathway with CT using standard planes. The purpose of this prospective, analytical, diagnostic accuracy study was to examine the accuracy of optic pathway contouring by veterinary radiation oncologists (ROs) before and after training on optic plane contouring. Optic pathway contours used as the gold standard for comparison were created based on expert consensus from registered CT and MRI for eight dogs. Twenty-one ROs contoured the optic pathway on CT using their preferred method, and again following atlas and video training demonstrating contouring on the optic plane. The Dice similarity coefficient (DSC) was used to assess contour accuracy. A multilevel mixed model with random effects to account for repeated measures was used to examine DSC differences. The median DSC (5th and 95th percentile) before and after training was 0.31 (0.06, 0.48) and 0.41 (0.18, 0.53), respectively. The mean DSC was significantly higher after training compared with before training (mean difference = 0.10; 95% CI, 0.08-0.12; P < 0.001) across all observers and patients. DSC values were comparable to those reported (0.4-0.5) for segmentation of the optic chiasm and nerves in human patients. Contour accuracy improved after training but remained low, potentially due to the small optic pathway volumes. When registered CT-MRI images are not available, our study supports routine addition of an optic plane with specific window settings to improve segmentation accuracy in mesaticephalic dogs ≥11 kg.
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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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".