Comparative Skull Stripping Techniques on Pediatric Magnetic Resonance Imaging
Bibliographic record
Abstract
Skull stripping (SS) is a well-known preprocessing step commonly adopted in Magnetic Resonance (MR) scans to eliminate non-brain tissues. While it is well-established for mid-adults, there is limited experimentation with the pediatric population. The continuous development of the pediatric brain presents challenges for existing SS techniques. This paper aims to compare different SS techniques for a pediatric population quantitatively and qualitatively. Due to the absence of manual brain segmentation (ground truth), the quantitative analysis focuses on pairwise Dice Similarity Coefficient (DSC) comparisons of SS techniques, while qualitative analysis was performed by visual inspection. The work also intends to detect potential outliers by thresholding DSC and visualizing SS results. We performed experiments with five techniques, Freesurfer (FS) versions 6.0.0, 7.1.1, 7.4.1, FSL-BET, and SynthStrip, using 279 T1-weighted MR scans from pediatric subjects. We observed the highest agreement between FSL-BET and SynthStrip, confirmed by the visual inspection. The different FS versions showed inconsistent and poor agreement with other techniques. Furthermore, we noticed complementary information for different SS techniques. For instance, when FSL-BET failed to remove the scalp, SynthStrip performed well in removing non-brain tissues and vice-versa. This suggests the potential benefits of combining these techniques into a consensus approach, such as ensembles in machine learning. In conclusion, the quantitative and qualitative analyses of different SS techniques for the pediatric population highlight the superiority of FSL-BET and SynthStrip and allow the identification of prospective outliers. The future exploration of ensemble techniques and tailored technique adaptations could lead to specialized SS techniques and more accurate brain analyses in pediatric subjects.
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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 | 0.001 |
| 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 | 0.001 |
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 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".