NSD_hypothalamus_atlas
Bibliographic record
Abstract
This study describes a high-resolution in-vivo magnetic resonance imaging atlas of the human hypothalamus.<br> <br> We employed a minimum deformation averaging (MDA) pipeline to produce a normalized (MNI152b), high-resolution template from multimodal (T1w and T2w) magnetic resonance imaging (MRI) datasets. 990 subjects derived from the HCP1200 date release were included in this study.<br> This template was used to delineate hypothalamic (n=13) and extrahypothalamic (n=12) gray and white matter structures.<br> <br> Files (in bold) of the hypothalamus atlas segmentation (tables (.csv) and images (nifti format)):<br> -a table with the structure name, abbreviation and label number:<br> <strong>Volumes_names-labels.csv</strong><br> -the full volume segmentation in 0.25 and 0.5 millimeter isotropic resolution:<br> <strong>atlas_labels_0.25mm.nii.gz</strong><br> <strong>atlas_labels_0.5mm.nii.gz</strong><br> -an archive of binary images of each structure separately:<br> <strong>isolated_nuclei_0.25mm_resolution.zip</strong><br> <br> The high-contrast, high-resolution MDA template images in 0.25 and 0.5 millimeter isotropic resolution:<br> <strong>MDA_990HCP_t1_MNI152b_0.25.nii.gz<br> MDA_990HCP_t1_MNI152b_0.5.nii.gz<br> MDA_990HCP_t2_MNI152b_0.25.nii.gz<br> MDA_990HCP_t2_MNI152b_0.5.nii.gz</strong><br> <br> Tables that describe the average structure volumes in the subjects used to generate the MDAs:<br> <strong>Average_left_right_hemispheric_hypothalamic_volumes_by_Sex.csv<br> Average_left_right_hemispheric_hypothalamic_volumes.csv</strong> <strong>Estimated_volumes_of_hypothalamus_proper_its_divisions_and_nuclei.csv</strong> Authors: Clemens Neudorfer, Jürgen Germann, Gavin J.B. Elias, Alexandre Boutet, Robert Gramer, Andres M. Lozano Affiliations:<br> Division of Neurosurgery, Depatment of Surgery, Toronto Western Hospital, University of Toronto, Canada.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.031 | 0.019 |
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; both teacher heads agree on what is shown here.
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".