Ex vivo human brain volumetry: Validation of MRI measurements
Bibliographic record
Abstract
Abstract Purpose The volume of in vivo human brains is determined with various MRI measurement tools that have not been assessed against a gold standard. The purpose of this study was to validate the MRI brain volumes by scanning ex vivo, in situ specimens, which allows the extraction of the brain after the scan to compare its volume with the gold‐standard water displacement method (WDM). Methods The 3T MRI T 2 ‐weighted, T 1 ‐weighted, and MP2RAGE images of seven anatomical heads fixed with an alcohol‐formaldehyde solution were acquired. The gray and white matter were assessed using two methods: (i) a manual intensity‐based threshold segmentation using Display (MINC‐ToolKit) and (ii) an automatic deep learning–based segmentation tool ( SynthSeg ). The brains were extracted and their volumes measured with the WDM after the removal of their meninges and a midsagittal cut. Volumes from all methods were compared with the ground truth (WDM volumes) using a repeated‐measures analysis of variance. Results Mean brain volumes, in cubic centimeters, were 1111.14 ± 121.78 for WDM, 1020.29 ± 70.01 for manual T 2 ‐weighted, 1056.29 ± 90.54 for automatic T 2 ‐weighted, 1094.69 ± 100.51 for automatic T 1 ‐weighted, 1066.56 ± 96.52 for automatic magnetization‐prepared 2 rapid gradient‐echo first inversion time, and 1156.18 ± 121.87 for automatic magnetization‐prepared 2 rapid gradient‐echo second inversion time. All volumetry methods were significantly different (F = 17.874; p < 0.001) from the WDM volumes, except the automatic T 1 ‐weighted volumes. Conclusion SynthSeg accurately determined the brain volume in ex vivo, in situ T 1 ‐weighted MRI scans. The results suggested that given the contrast similarity between the 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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".