P2‐222: Tracking brain ventricle expansion in Alzheimer's disease using combined intensity and shape‐based segmentation
Bibliographic record
Abstract
Brain ventricle volume measured from high-resolution magnetic resonance imaging (MRI) is a sensitive measure of atrophy in subjects aging normally, those with mild cognitive impairment (MCI), or those with Alzheimer disease (AD). The purpose of this work was to develop a novel automated segmentation algorithm that increased the accuracy and precision of brain ventricle segmentation compared to existing methods allowing images to be processed quickly without bias. A novel algorithm was implemented that combined automated seed point placement with intensity texture-based fuzzy connectedness segmentation and shape-based Expectation Maximization (EM) segmentation. On occasions of leakage into the third and fourth ventricle, manual cropping was performed but will soon be replaced by automated cropping. The algorithm was initially validated using 3D T1-weighted MP-RAGE images of a realistic brain ventricle phantom. The algorithm was further tested in 25 normal elderly subjects, 19 subjects with MCI and 24 subjects with AD utilizing 1.5T T1-weighted MP-RAGE images acquired at baseline and 24 months. Subjects were randomly selected from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Change over time was determined by repeated measures ttest while differences between groups was determined by one-way ANOVA (P <0.05 considered significant). The brain ventricle phantom volume was determined within 0.8% of its known volume. Baseline ventricle volume (average ± SEM) was 35.3 ± 2.7 cm 3 for normal elderly, 46.0 ± 6.3 cm 3 for MCI and 50.3 ± 6.5 cm 3 for AD. A significant ventricle volume increase was detected over 24 months in MCI (P <0.001) and AD subjects (P <0.001), but not in normal elderly (P = 0.12). The incorporation of the EM segmentation increased ventricle volume by 5-6% due to inclusion of the temporal horn regions and increased the observed 24 month differences within groups by 22-90%.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".