Semi-Automated Segmentation of Multiple Sclerosis Lesions in Brain MRI using Texture Analysis
Bibliographic record
Abstract
Reliable segmentation of multiple sclerosis lesions in magnetic resonance brain imaging is important for at least three types of practical applications: pharmaceutical trials, decision making for drug treatment or surgery, and patient follow-up. Manual segmentation of the MS lesions in brain MRI by well qualified experts is usually preferred. However, manual segmentation is hard to reproduce and can be time consuming in the presence of large volumes of MRI data. On the other hand, automated segmentation methods are significantly faster and yield reproducible results. However, these automated methods generally produce segmentation results that agree only partially with the ground truth segmentation provided by the experts. They also suffer from miss-classification errors, especially false-negative miss-classification where true lesions are left undetected, which is a grave concern from a medical point of view. In this work, we propose a semi-automated MS lesion detection system that combines the knowledge of the expert with the computational capacity to produce faster and more reliable MS segmentation results. In particular, the user selects coarse regions of interest (ROIs) that may potentially contain MS lesions. Then any MS lesions within the provided ROI's are then detected and segmented based on texture analysis. The method is applied on real MRI data and the results are qualitatively compared to a ground truth, which is manually segmented by a human expert
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".