Longitudinal White Matter Hyperintensity Segmentation with LSTM-Enhanced U-Net
Bibliographic record
Abstract
White matter hyperintensities (WMHs) are commonly accepted biomarkers of brain aging and neurodegeneration, typically observed on magnetic resonance imaging. Segmenting WMHs using U-Net-based convolutional neural networks (CNNs) is a viable and widely demonstrated technique to automate an otherwise laborious and tedious manual task. Although automation of cross-sectional WMH segmentation has been demonstrated, failure to appropriately process temporal information may decrease accuracy and limit understanding of lesion progression. Here, we investigate the combination of two convolutional long short-term memory (LSTM) approaches and U-Net models to segment WMHs in young and old adults. Using information acquired at two visits in 50 healthy individuals, we analyzed model-task performance using$F$-measure of three variants: 1) baseline standard U-Net, 2) Partial LSTM U-Net, and 3) Full LSTM U-Net. Results showed that while LSTM-based models outperform the baseline model in$F$-measure, notable differences emerged in how effectively they detected WMHs of varying size and distribution between age groups. These findings highlight the need to account for age-related heterogeneity in lesion morphology when designing and evaluating automated longitudinal WMH segmentation tools, and suggest that age- and size-aware modeling may lead to more robust predictions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".