Nonlocal spatial clustering in automated brain hematoma and edema segmentation
Bibliographic record
Abstract
Abstract Hematoma and edema volume are potential predictors of 30‐day mortality rate and functional outcome (degree of disability or dependence in daily activities after a stroke) for patients with intracerebral hemorrhage. The manual segmentation of hematoma and edema from computed tomography scans is common practice but a time‐consuming and labor‐intensive task. Automated segmentation is an appealing alternative, but it is challenging because of the poorly defined boundary between edema and the surrounding healthy brain tissue. There is limited literature on this problem, and we aim to help fill the gap between the theoretical development of segmentation methods and the practical need. Our framework is fully automated and requires no supervision. The method uses nonlocal regularized spatial fuzzy C‐means clustering in the initialization stage and the active contour without edges method in the refinement stage. To evaluate it, we used 30 subjects with different sizes, shapes, and locations of hematoma and edema. Compared with the manual segmentation results from two independent raters, our method performs hematoma segmentation well, with an average dice score coefficient of 0.92. Although there is a lack of ground truth in edema segmentation due to the high inter and intrarater variation, our results are comparable with manual segmentation results.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".