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Record W1988479807 · doi:10.1117/12.428064

<title>Correlation of preoperative MRI and intraoperative 3D ultrasound to measure brain tissue shift</title>

2001· article· en· W1988479807 on OpenAlexaff
David G. Gobbi, Belinda K. H. Lee, Terence M. Peters

Bibliographic record

VenueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2001
Typearticle
Languageen
FieldComputer Science
TopicMedical Image Segmentation Techniques
Canadian institutionsWestern University
Fundersnot available
KeywordsUltrasound3D ultrasoundImage warpingComputer scienceInterpolation (computer graphics)Artificial intelligenceComputer visionVolume (thermodynamics)MedicineRadiologyImage (mathematics)Physics

Abstract

fetched live from OpenAlex

B-Mode ultrasound is often used during neurosurgery to provide intra-operative images of the brain though a craniotomy, but the use of 3D ultrasound during surgery is still in its infancy. We have developed a system that provides real-time freehand 3D ultrasound reconstruction at a reduced resolution. The reconstruction proceeds incrementally and the 3D image is overlayed, via a computer, on a pre-operative 3D MRI scan. This provides the operator with the necessary feedback to maintain a constant freehand sweep-rate, and also ensures that the sweep covers the desired anatomical volume. All of the ultrasound video frames are buffered, and a full-resolution, compounded reconstruction proceeds once the manual sweep is complete. We have also developed tools for manual tagging of homologous landmarks in the 3D MRI and 3D ultrasound volumes that use a piecewise cubic approximation of thin-plate spline interpolation to achieve interactive nonlinear registration and warping of the MRI volume to the ultrasound volume: Each time a homologous point-pair is identified by the use, the image of the warped MRI is updated on the computer screen after less than 0.5 s.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.635
Threshold uncertainty score0.513

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.010
GPT teacher head0.250
Teacher spread0.240 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations15
Published2001
Admission routes1
Has abstractyes

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Same venueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIESame topicMedical Image Segmentation TechniquesFrench-language works237,207