MRI tissue segmentation incorporating a bias field modulated smoothness prior
Bibliographic record
Abstract
This paper examines a refinement to probabilistic intensity based tissue segmentation methods, which makes use of knowledge derived from an MRI bias field estimate. Intensity based labeling techniques have employed local smoothness priors to reduce voxel level tissue labeling errors, by making use of the assumption that, within uniform regions of tissue, a voxel should be highly likely to have a similar tissue assignment to its neighbors. Increasing the size of this neighborhood provides more robustness to noise, but reduces the ability to describe small structures. However, when intensity bias due to RF field inhomogeneity is present within the MRI data, local contrast to noise may vary across the image. We therefore propose an approach to refining the labeling by making use of the bias field estimate, to adapt the neighborhood size applied to reduce local labeling errors. We explore the use of a radially symmetric Gaussian weighted neighborhood, and the use of the mean and median of the adapted region probabilities, to refine local probabilistic labeling. The approach is evaluated using the Montreal brainweb MRI simulator as a gold standard providing known gray, white and CSF tissue segmentation. These results show that the method is capable of improving the local tissue labeling in areas most influenced by inhomogeneity. The method appears most promising in its application to regional tissue volume analysis or higher field MRI data where bias field inhomogeneity can be significant.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".