A statistical atlas-based technique for automatic segmentation of the first Heschl's gyrus in human auditory cortex from MR images
Bibliographic record
Abstract
We present an automatic method for the segmentation of the first transverse temporal gyrus of Heschl (HG), the morphological marker for primary auditory cortex in humans. The proposed technique utilizes a statistical anatomical atlas of the gyrus, generated from a set of training samples using principal component analysis. The training set consists of MRI data from 12 subjects with the corresponding Heschl's gyri manually labeled in each hemisphere (separate atlases were generated for each hemisphere). We used a leave-one-out approach to automatically segment Heschl's gyri in both hemispheres from the MR image data using generated atlases. We assessed the accuracy of this atlas-based technique by using it to segment the HG region from several test cases and finding the overlap between the segmented and labeled HG regions. Results demonstrated more than 75% and 83% accuracy in the extraction of the HG volumes in the left and right hemispheres, respectively. It is expected that the proposed tool can be adapted to extract other anatomical regions in the brain.
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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 | 0.002 | 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) | 0.002 | 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".