Multi-seed segmentation of the primary tumor mass in neuroblastoma using opening-by-reconstruction
Bibliographic record
Abstract
Segmentation of the primary tumor mass in neuroblastoma is an important problem. Segmentation could aid the radiologist by facilitating reproducible, objective quantification of the tumor's tissue composition and volume. However, segmentation continues to be a difficult problem because the tumor is usually composed of inhomogeneous tissue types, some of which possess strong similarities in computed tomographic characteristics to contiguous nontumoral tissues. Previous work on the segmentation of the primary tumor mass focused on the removal of various problematic tissues prior to segmentation using fuzzy connectivity. Although the results showed promise, further work is required to minimize leakage of the result of segmentation. Opening-by-reconstruction is introduced in this work as a viable substitute for fuzzy connectivity because of its similarity to and computational advantages over the latter; furthermore, multi-seed segmentation can be incorporated into the procedure. The results suggest that opening-by-reconstruction is a more efficient segmentation method than fuzzy connectivity for the neuroblastic tumor, and is more suitable for the multi-seed approach.
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How this classification was reachedexpand
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 | 0.000 |
| 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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".