Beyond cost function masking: RPCA-based non-linear registration in the context of VLSM
Bibliographic record
Abstract
Voxel-based lesion symptom mapping (VLSM) allows studying the relationship between stroke location and clinical outcome. The core idea of VLSM is to map all patient cases into a common atlas space and then apply statistical tests on a voxel level comparing outcome measures of patients with a lesion in the voxel to those without lesion. A major limitation of VLSM is that it requires a previous lesion segmentation, which is mostly performed manually, for masked subject-to-atlas registration as well as for the VLSM analysis. The aim of this work is to evaluate the feasibility of a recently introduced robust PCA (RPCA)-based iterative non-linear registration framework that potentially overcomes this limitation by generating the lesion segmentation on the fly. In addition, we propose and evaluate a rapid variant of this framework (successively tightened low rank-condition RPCA, stl-RPCA). Based on 29 follow-up FLAIR datasets of patients with ischemic stroke, the lesion segmentation capabilities and subject-to-atlas transformation properties of the RPCA methods are evaluated and compared to non-linear registration with and without cost function masking. Results reveal that the proposed method is capable of segmenting the lesions with an average Dice coefficient of 63%. Similar to nonlinear registration with cost function masking, the RPCA-based registration frameworks significantly decrease confounding effects of pathologies on subject-to-atlas transformation properties. Overall, the RPCA frameworks lead to promising results and could considerably enhance VLSM analyses.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".