Segmenting White Matter Hyperintensity in Alzheimer’s Disease using U-Net CNNs
Bibliographic record
Abstract
White-matter hyperintensity (WMH) is associated with many disorders where it is suggestive of underlying cerebrovascular, small-vessel disease pathology. However, its role in Alzheimer’s disease (AD), mixed, and vascular dementia remains an open area of research. The fluid-attenuated inversion recovery (FLAIR) magnetic resonance (MR) imaging sequence is commonly used to visualize WMH because it provides good image contrast, not only between WMH and normal tissue, but also between WMH and cerebrospinal fluid. Manual segmentation of WMH lesions in brain volumes, on a slice-by-slice basis, is time-consuming with high inter-rater variability, however, this process remains the broadly accepted gold standard. In this study, variations of 2D, 2.5D and 3DU-shaped convolutional neural networks (U-Net CNNs) were used to perform semantic segmentation on FLAIR images. We evaluated these models in brain volumes obtained from 186 individuals from one of three disease classes: healthy normal (N = 94), mild cognitive impairment (N = 55), and AD (N = 37). Four common architectures (VGG16, VGG19, ResNet and EfficientNetBO) were employed as feature extractors. Results were assessed across the whole brain and by brain region (frontal, occipital, parietal, temporal lobes plus the insula) to identify differences in performance. In general, the predicted WMH volumes had an F-measure score >95% on the test data compared to manual segmentation. This work identified that 2. 5D with either VGG16 or VGG19 was the most suitable configuration when segmenting WMH. WMH segmentation performance and measured volume was found to vary between regions and disease classes. U-Net CNN architectures have good performance and may provide valuable insights about the white matter pathology.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".