Longitudinal, Regional and Deformation-Specific Corpus Callosum Shape Analysis for Multiple Sclerosis
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Bibliographic record
Abstract
The corpus callosum (CC) is an anatomical structure which connects the two brain hemispheres. Neurological diseases can cause atrophy of the CC resulting in a change in its size and shape. The measurement and analysis of this change is one of the goals of clinical research. We perform statistical analysis of the shape of the CC extracted from MR brain scans of a group of multiple sclerosis patients undergoing a longitudinal (serial) study. In contrast to the classical boundary-based, global shape variability measures, e.g. principal component analysis (PCA) of CC boundary vertices, we perform a deformation-specific PCA for analyzing the global and regional shape of the CC. This deformation-specific PCA is based on a medial-based shape representation. The adopted shape representation describes shape variability in terms of intuitive deformations (e.g. bending, stretching and thickness). We present qualitative and quantitative results for 412 MR images of the CC. We show that our method is successful in identifying and quantifying the effect of each type of deformation on the shape variability of the CC. In addition to analyzing the spatial shape variability in the CC, we explore shape changes as the disease progresses. Our method allows the exploration of the shape variability quantitatively (e.g. the amount of variance explained by a particular principal mode of shape variation) as well as in a qualitative visual manner (e.g. by visualizing, say, the 2nd principal mode of shape variation due to bending at the 4th sub-region of the CC) which is useful for developing an intuitive understanding of the effects of MS on the CC shape.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 it