IC‐P‐099: A quantitative comparison between two manual hippocampal segmentation protocols
Bibliographic record
Abstract
Hippocampal atrophy is an important morphological biomarker of disease progression in Alzheimer's disease. The hippocampal harmonization project (HarP) resolves heterogeneity between many different hippocampal segmentation protocols by designing a single agreed-upon segmentation protocol (Boccardi, 2014). In this study, we compared HarP to the protocol presented by Pruessner (2000) in terms of both (1) segmentation accuracy and (2) the mean volume hippocampal difference between the ADNI healthy and AD population. Both manual segmentation methods were used to train a fully automated patch-based segmentation technique (Fonov et al. 2012). The method shows promising results in terms of conformity with training library (Zandifar, AAIC 2014:IC-P-150). For HarP, the 100 released HarP datasets were used (Boccardi, 2015). For the Pruessner protocol, 60 subjects from ADNI data (20 NC; 20 MCI; 20 AD) were segmented by a single rater. All baseline visit ADNI1 NC and AD subjects with 1.5T scans were segmented automatically. Kappa overlap was used to evaluate segmentation accuracy in a leave-one-out validation. Cohen's d effect size was measured between the hippocampal volume in both clinical groups in the native and stereotactic space. The average Kappa overlap was 0.878 for the Pruessner protocol and 0.843 for HarP. Table 1 shows Cohen's d effect size between the groups in both stereotaxic and native space for each side. The results show that both protocols capture the statistical difference between the group means (Cohen's d > 0.8). The difference in effect size between segmentation methods is negligible in both native and stereotaxic space. Both segmentation protocols yield reasonably accurate segmentations, and have equivalent power to show NC: AD group differences. The larger kappa for Pruessner labels is due in part to tracing in 3D versus 2D for HarP labels, where boundary smoothness may not be ensured. Among the many protocols compared in (Boccardi, 2014), the Pruessner protocol is the most similar to HarP, thus both methods work equally well to show group differences. Given its good performance, HarP is a good candidate to resolve heterogeneity between hippocampal segmentation protocols while maintaining power to detect volume differences between groups.
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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 | 0.026 | 0.082 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".