Robust deformable registration of pre- and post-resection ultrasound volumes for visualization of residual tumor in neurosurgery
Bibliographic record
Abstract
The brain tissue deforms significantly during neurosurgery, which has led to the use of intra-operative ultrasound in many sites to provide updated ultrasound images of tumor and critical parts of the brain. Several factors degrade the quality of post-resection ultrasound images such as hemorrhage, air bubbles in tumor cavity and the application of blood-clotting agent around the edges of the resection. As a result, registration of post- and pre-resection ultrasound is of significant clinical importance. In this paper, we propose a nonrigid symmetric registration (NSR) framework for accurate alignment of pre- and post-resection volumetric ultrasound images in near real-time. We first formulate registration as the minimization of a regularized cost function, and analytically derive its derivative to efficiently optimize the cost function. We use Efficient Second-order Minimization (ESM) method for fast and robust optimization. Furthermore, we use inverse-consistent deformation method to generate realistic deformation fields. The results show that NSR significantly improves the quality of alignment between pre- and post-resection ultrasound images.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".