Volumetry of Temporopolar, Perirhinal, Entorhinal and Parahippocampal Cortex from High-resolution MR Images: Considering the Variability of the Collateral Sulcus
Bibliographic record
Abstract
Researchers in clinical and basic neuroscience frequently target structures of the human medial temporal lobe (MTL) for volumetric analysis with magnetic resonance imaging (MRI). In neurodegenerative diseases, a precise volumetric analysis of MTL structures can assist in differential diagnosis and can be used in guiding early treatment. Also, in functional neuroimaging, exact localization is crucial for the correct interpretation of focal MTL activations with respect to specific memory functions. In presently available protocols, precise and consistent volumetric analysis of MTL structures is compromised in numerous ways. Most importantly, in order to cover all structures of the MTL, the researcher is presently forced to combine independently developed segmentation protocols for different structures from different laboratories. This approach limits anatomical precision because these protocols are based on different anatomical guidelines and descriptions that cannot easily be integrated. The segmentation approach presented in this paper was designed to address this issue by presenting segmentation guidelines for all major structures of the parahippocampal gyrus (PHG). It was developed directly to complement a volumetric protocol for hippocampus and amygdala (Pruessner et al., 2000, Cereb Cortex 10:433-442), thus allowing volumetric assessment of all major MTL structures in an integrated and consistent manner. Furthermore, it takes into consideration the neuroanatomical appearance of the collateral sulcus by presenting a method to correct the volumes of the surrounding cortices for the variability of this sulcus. The protocol was validated using MR images of 40 healthy normal control subjects (20 men and 20 women, age range 18-42 years). Intra- and interrater coefficients are presented, together with mean values for the volumes of all PHG structures, correlations with age and sex, and tests for hemispheric differences.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 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".