Deformable Atlases for the Segmentation of Internal Brain Nuclei in Magnetic Resonance Imaging
Bibliographic record
Abstract
Magnetic resonance imaging (MRI) is commonly employed for the depiction of soft tissues, most notably the human brain. Computer-aided image analysis techniques lead to image enhancement and automatic detection of anatomical structures. However, the information contained in images does not often offer enough contrast to robustly obtain a good detection of all internal brain structures, not least the deep grey matter nuclei. We propose a method that incorporates prior anatomical knowledge in the shape of digital atlases that deform to fit the image data to be analysed. Our technique is based on a combination of rigid, affine and non-rigid registration, segmentation of key anatomical landmarks and propagation of the information of the atlas to detect deep grey matter nuclei. The Montreal Neurological Institute (MNI) and Zubal atlases are employed. Results show that detecting important structures such as the ventricles and brain outlines greatly improves the results. Our method is fully automatic.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.003 | 0.002 |
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".