Texture analysis and morphological processing of magnetic resonance imaging assist detection of focal cortical dysplasia in extra-temporal partial epilepsy
Bibliographic record
Abstract
In many patients, focal cortical dysplasia (FCD) is characterized by minor structural changes that may go unrecognized by standard radiological analysis. To increase the sensitivity of magnetic resonance imaging (MRI) for the detection of subtle lesions of FCD, we developed voxel-based image postprocessing methods, including first-order texture analysis and morphological processing modeled on known MRI features of FCD. We selected 16 patients with histologically proven FCD. Image processing features were calculated over a neighborhood for each voxel in the three-dimensional T1-weighted MRI. Three feature maps were generated: (1) gray matter thickness map to model cortical thickening, (2) gradient map to model blurring of the gray matter-white matter junction, and (3) relative intensity map to model the hyperintense signal within the lesion. Two observers detected lesions on conventional MRI in 8/16 and on ratio maps in 14/16 patients. Sensitivity was 87.5% (14/16) for the ratio maps compared to 50% (8/16) for MRI (p < 0.003). Specificity was 95% (19/20) for ratio maps and 100% (20/20) for MRIs. Cohen's kappa was 0.53 for MRIs, indicating moderate agreement, and 0.83 for ratio maps, indicating strong agreement beyond chance between the 2 observers. The image-processing methods developed in this study improve visual detection of FCD, even in cases where no lesion is obvious on MRI. These techniques could increase the number of patients with partial epilepsy who could benefit from surgery.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 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".