Volumetry of Hippocampus and Amygdala with High-resolution MRI and Three-dimensional Analysis Software: Minimizing the Discrepancies between Laboratories
Bibliographic record
Abstract
Within the medial temporal lobe, both the hippocampus and amygdala are frequently targeted by researchers and clinicians for volumetric analysis based on magnetic resonance imaging (MRI). However, different data acquisition techniques, analysis software and anatomical boundaries have in the past made it difficult to compare results of MRI studies from different laboratories. In order to reduce these differences, a segmentation protocol was established with 40 healthy normal control subjects recently scanned in our laboratory. Data acquisition was performed with a three-dimensional gradient echo technique, and scans were corrected for non-uniformity and registered into standard stereotaxic space prior to segmentation. Volumetric analysis was performed manually using three-dimensional software that allows simultaneous analysis of sagittal, coronal and horizontal images. Intra- and inter-rater coefficients yielded correlation coefficients comparable with other protocols. The hippocampal volume was larger in the right hemisphere (3324 versus 3208 mm(3)), while no interhemispheric differences for the amygdala (1154 versus 1160 mm(3)) could be observed. Most importantly, results from recent segmentation protocols for hippocampus and amygdala seem to approach each other with regard to mean volumes and interhemispheric differences. This indicates that the advances in scanning technique, volume preparation and segmentation protocols allow a more precise definition of medial temporal lobe structures with MRI, and that results for mean volumes for hippocampus and amygdala from different laboratories will eventually become comparable.
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".