Fully Automated Glioblastoma Segmentation and Classification in Multispectral Magnetic Resonance Images Based on Level Set and Deep Neural Network
Bibliographic record
Abstract
ABSTRACT Automatic segmentation and classification methods of glioblastomas in magnetic resonance imaging (MRI) scans are essential to overcome the limitations of error‐prone manual methods, especially given the intrinsic challenges such as intensity nonuniformity, diverse anatomical brain alterations, and significant variations in tumor shape, size, and location. These complexities pose major hurdles for radiologists in diagnosis and surgical planning, underscoring the critical significance of robust automated solutions. This study presents a novel fully automated approach for segmentation and classification of glioblastoma brain tumors using multi‐spectral MRI data. Our proposed framework innovatively integrates two key steps. In the first step, a new level set method is presented for segmentation, which is uniquely enhanced by super‐pixel fuzzy entropy‐based clustering—a technique designed to effectively handle image inhomogeneities and noise—density peak clustering, and a lattice Boltzmann solver for efficient contour evolution. In the second step, a VGG‐16 deep neural network is employed for precise classification. To assess the capability of the proposed method in both segmentation and classification tasks, real T2‐weighted and fluid‐attenuated inversion recovery magnetic resonance images of glioblastomas from the BraTS 2020 dataset are used simultaneously in a multi‐spectral manner. Our segmentation results, evaluated by measuring the Dice coefficient, Jaccard index, sensitivity, specificity, and running time. The mean values (Mean ± Standard deviation) of these metrics are 0.8915 ± 0.0293, 0.8055 ± 0.0478, 0.9535 ± 0.0644, 0.9910 ± 0.0364, 2.2909 ± 0.2597, respectively. Additionally, the average values of accuracy, precision, recall, and F1‐score across the fivefold cross‐validation of the classification method are 0.9149, 0.9532, 0.9160, and 0.9349, respectively. According to the experiments, our proposed fully automated framework not only achieves superior performance in simultaneous segmentation and classification compared to other state‐of‐the‐art segmentation methods but also offers a robust and efficient solution for clinical applications. While this study demonstrates strong potential, future work will focus on extending the framework for multi‐label segmentation of different tumor sub‐regions and validating its efficacy on even larger and more diverse clinical datasets.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".