Computer-aided lateral ventricular and brain volume measurements in 3D ultrasound for assessing growth trajectories in newborns and neonates
Bibliographic record
Abstract
For newborns and neonates, ultrasound (US) is the most common imaging modality used for examinations due to its accessibility and ease of use. However, precise volume measurements remain limited in 2D, while MRI in newborns is typically avoided because of immobilization issues which may require sedation. The objective of this study is to assess and validate the lateral ventricular and total brain volumes obtained with an automatic segmentation method using cerebral trans-fontanelle 3D US. Infants aged between 2 and 8.5 months old were recruited, with both MRI and 3D US acquired on the same day was used to validate ventricular and brain volume measurements in comparison to MRI. Lateral ventricles were segmented on both the US (manually and with a proposed automatic fusion-based approach) and MRI, while brain volumes were estimated with an automatic segmentation method. Volumetric 3D US measurements were then evaluated with respect to age distribution. For the comparison between MRI and 3D US, strong inter-class correlations (ICC) were found for the ventricle volumes (manual: 5.9% ± 2.5% difference (ICC = 0.99); automatic: 6.0% ± 2.6% difference (ICC = 0.98)), as well as the total brain size, with a 3.0% ± 1.3% difference (ICC = 0.98). There was no statistically significant difference based on t-test and f-test for the lateral ventricles volume (t-test: p = 0.542) and (f-test: p = 0.738) and for the total brain volume (t-test: p = 0.412) and (f-test: p = 0.685) between MRI and 3D US. This study demonstrates that 3D US can be used to automatically assess lateral ventricular and total brain volumes with no significant difference to the MRI acquisitions. The highest correlations were obtained for infants under 8 months when the fontanelle is open.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".