Automated Brain Tumor Segmentation Based on Multi-Planar Superpixel Level Features Extracted From 3D MR Images
Bibliographic record
Abstract
Brain tumor segmentation from Magnetic Resonance Imaging (MRI) is of great importance for better tumor diagnosis, growth rate prediction and radiotherapy planning. But this task is extremely challenging due to intrinsically heterogeneous tumor appearance, the presence of severe partial volume effect and ambiguous tumor boundaries. In this work, a unique approach of tumor segmentation is introduced based on superpixel level features extracted from all three planes (x -y, y - z, and z - x) of 3D volumetric MR images. In order to avoid the pixel randomness and to account for precise inhomogeneous boundaries of brain tumor, each of the images belonging to a particular plane is partitioned into irregular patches (superpixels) based on their intensity and spatial similarity. Next, various statistical and textural features are extracted from each superpixel where all three planes are considered separately in order to obtain better labeling on superpixels in tumor edges. A feature selection scheme is proposed based on their performance on histogram based consistency analysis and local descriptor pattern analysis, which offers a significant reduction in feature dimension without sacrificing classification performance. For the purpose of supervised classification, Extremely Randomized Trees is used to classify these superpixels into a tumor or a non-tumor class. Finally, pixel level decision is taken based on corresponding decisions obtained in each plane. Extensive simulations are carried out on publicly available dataset and it is found that the proposed method offers better tumor segmentation performance in comparison to that obtained by some state of the art methods.
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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 | 0.000 | 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 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".