<i>Ex vivo</i> human brain volumetry: validation of magnetic resonance imaging measurements
Bibliographic record
Abstract
Abstract Background Neurodegenerative diseases are associated with brain atrophy. The volume of in vivo human brains is determined with various magnetic resonance imaging (MRI) measurement tools of which the validity has not been assessed against a gold standard. Here, we propose to validate the MRI brain volumes by scanning ex vivo-in situ specimens (i.e., anatomical heads), which allows the extraction of the brain after the scan to compare its volume with the gold standard water displacement method (WDM). Methods We acquired 3T MRI T2-weighted, T1-weighted, and MP2RAGE images of seven anatomical heads fixed with an alcohol-formaldehyde solution routinely used in anatomy laboratories and segmented the gray and white matter of the brain using two methods: 1) a manual intensity-based threshold segmentation using Display (MINC-ToolKit, McConnell BIC), and 2) an automatic Deep-Learning-based segmentation tool (SynthSeg). The brains were then extracted, and their volumes were measured with the WDM after the removal of their meninges and a midsagittal cut (to allow water penetration into the ventricles). Volumes from all methods were compared to the ground truth (WDM volumes) using a repeated-measures ANOVA. Results Mean brain volumes, in cubic centimeters, were 1111.14±121.78 for WDM, 1020.29±70.01 for manual T2-weighted, 1056.29±90.54 for automatic T2-weighted, 1094.69±100.51 for automatic T1-weighted, 1066.56±96.52 for automatic MP2RAGE INV1, and 1156.18±121.87 for MP2RAGE INV2. All volumetry methods were significantly different (F=17.874; p<0.001) from the WDM volumes, except the automatic T1-weighted volumes. Conclusion We demonstrate that SynthSeg accurately determines the brain volume in ex vivo-in situ T1-weighted MRI scans. Our results also suggest that given the contrast similarity between our ex vivo and in vivo sequences, the brain volumes of clinical studies are most probably sufficiently accurate, with some degree of underestimation depending on the sequence used.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".