Deep learning segmentation of orbital fat to calibrate conventional MRI for longitudinal studies
Bibliographic record
Abstract
In conventional non-quantitative magnetic resonance imaging, image contrast is consistent within images, but absolute intensity can vary arbitrarily between scans. For quantitative analysis of intensity data, images are typically normalized to a consistent reference. The most convenient reference is a tissue that is always present in the image, and is unlikely to be affected by pathological processes. In multiple sclerosis neuroimaging, both the white and gray matter are affected, so normalization techniques that depend on brain tissue may introduce bias or remove biological changes of interest. We introduce a complementary procedure, image “calibration,” the goal of which is to remove technical intensity artifacts while preserving biological differences. We demonstrate a deep learning approach to segmenting fat from within the orbit of the eyes on T1-weighted images at 1.5 and 3 T to use as a reference tissue, and use it to calibrate 1018 scans from 256 participants in a study of pediatric-onset multiple sclerosis. The machine segmentations agreed with the adjudicating expert (DF) segmentations better than did those of other expert humans, and calibration resulted in better agreement with semi-quantitative magnetization transfer ratio imaging than did normalization with the WhiteStripe1 algorithm. We suggest that our method addresses two key priorities in the field: (1) it provides a robust option for serial calibration of conventional scans, allowing comparison of disease change in persons imaged at multiple time points in their disease; and (ii) the technique is fast, as the deep learning segmentation takes only 0.5 s/scan, which is feasible for both large and small datasets.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".