Automated brain tissue segmentation and MS lesion detection using fuzzy and evidential reasoning
Bibliographic record
Abstract
This paper presents a fuzzy and evidential reasoning approach for segmenting main brain tissues: white matter (WM), grey matter (GM), and cerebrospinal fluid (CSF), as well as detecting multiple sclerosis lesions (MS) based on multi-modality MR images. The method performs intensity based tissue segmentation using a fuzzy Dempster-Shafer evidential reasoning data fusion scheme while MS lesions are detected by means of a fuzzy inferencing scheme. The approach is fully automated and unsupervised. Experiments have been carried out for segmenting 15 axial slices of multi-modality MR images obtained from the Simulated Brain Database (SBD). The average overall accuracy is 96.77% for segmenting tissues CSF, GM, and WM. The average sensitivity is 84.34%, and the average similarity index is 81.94% in MS detection in terms of ground truth images.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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 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".