High-resolution segmentation of the cavum septum pellucidum in young adult human brains
Bibliographic record
Abstract
The cavum septum pellucidum (CSP) is a small cerebrospinal fluid-filled space found between the lateral ventricles of the forebrain that is often used as a biomarker for neurological disease and brain injury. The incidence of the CSP varies widely in different studies, with many reports finding that the CSP is frequently absent in healthy brains. Variables such as race, age and sex are typically not well-reported in CSP studies, presenting a challenge to understanding the normal distribution of the CSP in adult human brains. Moreover, the small size and frequently indistinct borders present a challenge for automated segmentation of the CSP. To address these issues, we developed a novel manual parcelation approach to volumetrically segment the CSP in high-resolution T1-weighted structural MRIs from male and female participants in the young adult dataset of the Human Connectome Project (HCP). We identified the CSP in 95.6% of subjects, compared to 57.1% when the automated segmentation approach was used on the same subjects. The CSP volume was significantly larger in male than female brains, both in terms of raw volume and volumes normalized for intracranial volume. To our knowledge, this study is the first to develop and validate a segmentation protocol for CSP volume, and to evaluate both the incidence and volume of the CSP in a representative population of young adults. Overall, these results provide a more accurate representation of the CSP in control populations, laying an improved foundation for its potential use as a biomarker for various disorders.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".