Iterative Multilevel MRF Leveraging Context and Voxel Information for Brain Tumour Segmentation in MRI
Why this work is in the frame
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Bibliographic record
Abstract
In this paper, we introduce a fully automated multistage graphical probabilistic framework to segment brain tumours from multimodal Magnetic Resonance Images (MRIs) acquired from real patients. An initial Bayesian tumour classification based on Gabor texture features permits subsequent computations to be focused on areas where the probability of tumour is deemed high. An iterative, multistage Markov Random Field (MRF) framework is then devised to classify the various tumour subclasses (e.g. edema, solid tumour, enhancing tumour and necrotic core). Specifically, an adapted, voxel-based MRF provides tumour candidates to a higher level, regional MRF, which then leverages both contextual texture information and relative spatial consistency of the tumour subclass positions to provide updated regional information down to the voxel-based MRF for further local refinement. The two stages iterate until convergence. Experiments are performed on publicly available, patient brain tumour images from the MICCAI 2012 [11] and 2013 [12] Brain Tumour Segmentation Challenges. The results demonstrate that the proposed method achieves the top performance in the segmentation of tumour cores and enhancing tumours, and performs comparably to the winners in other tumour categories.
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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 | 0.001 |
| 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 | 0.001 |
| 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 it