Label‐fusion‐segmentation and deformation‐based shape analysis of deep gray matter in multiple sclerosis: The impact of thalamic subnuclei on disability
Bibliographic record
Abstract
Deep gray matter (DGM) atrophy has been reported in patients with multiple sclerosis (MS) already at early stages of the disease and progresses throughout the disease course. We studied DGM volume and shape and their relation to disability in a large cohort of clinically well-described MS patients using new subcortical segmentation methods and shape analysis. Structural 3D magnetic resonance images were acquired at 1.5 T in 118 patients with relapsing remitting MS. Subcortical structures were segmented using a multiatlas technique that relies on the generation of an automatically generated template library. To localize focal morphological changes, shape analysis was performed by estimating the vertex-wise displacements each subject must undergo to deform to a template. Multiple linear regression analysis showed that the volume of specific thalamic nuclei (the ventral nuclear complex) together with normalized gray matter volume explains a relatively large proportion of expanded disability status scale (EDSS) variability. The deformation-based displacement analysis confirmed the relation between thalamic shape and EDSS scores. Furthermore, white matter lesion volume was found to relate to the shape of all subcortical structures. This novel method for the analysis of subcortical volume and shape allows depicting specific contributions of DGM abnormalities to neurological deficits in MS patients. The results stress the importance of ventral thalamic nuclei in this respect.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".