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 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".