Segmentation of GBM in MRI images using an efficient speed function based on level set method
Bibliographic record
Abstract
Accurate segmentation and characterization of abnormalities in brain tumor are challenging task, especially in the case of GBM tumors, where the ambiguities presented in the boundaries of these tumors necessitates using efficient segmentation method. Level set methods have proven to be a flexible and powerful tool for image segmentation because of being shape-driven method with a properly defined speed function to grow or shrink the boundaries to segment complex objects of interest, precisely. In this study a combined level set algorithm consists of both region and boundary terms for GBM segmentation is proposed. The modified speed function incorporates threshold based level set and the Laplacian filter to highlight the fine details for performing an accurate extraction of the tumor region using multiple seed points selected by the user. An evaluation was performed on a dataset containing 6 patients with GBM by using three measures Dice, false positive error (FPE) and false negative error (FNE). Manual segmentation of GBM is considered as gold standard. Compared to traditional method, the mean of FPE and FNE are improved by 53.5% and 53.1%, respectively. The mean of Dice coefficients between our results and gold standard measurement reached to 0.88. As the results proved, the proposed combined method improves the accuracy of GBM segmentation by 16% compared to conventional level set method with threshold based speed function. Our method is also robust to change of parameters.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".