Vascular tree extraction from MRA and power Doppler US image volumes
Bibliographic record
Abstract
Image-guided neurosurgery procedures rely on the assumption that the brain is a rigid body for proper pre-operative image registration. However, the brain tissues typically shift prior to and during the procedures. Intra-operative ultrasound images may be used to identify the extent of brain shift and to correct the pre-operative magnetic resonance (MR) images. We propose to use the features of the vascular tree as common landmarks between the MR and ultrasound image volumes to properly update the pre-operative MR images. We present the preliminary results of a 3D method to extract the vascular tree from magnetic resonance angiogram and power Doppler ultrasound image volumes. The faces of a cubic region of interest are searched to measure the curvature of the vessel and to identify branches. The vascular tree is constructed by interpolating between the vessel points that are determined as the algorithm iterates through the data volume. Computer reproductions of the extracted skeleton correspond well to the vessels in magnetic resonance angiograms and in power Doppler ultrasound volumes.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".