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Record W2032009698 · doi:10.1109/iembs.2010.5627807

Edge-based partial volume averaging estimation for FLAIR MRI with white matter lesions

2010· article· en· W2032009698 on OpenAlexaff
April Khademi, A. Venetsanopoulos, Alan R. Moody

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicMedical Image Segmentation Techniques
Canadian institutionsSunnybrook Health Science CentreHealth Sciences CentreToronto Metropolitan UniversityUniversity of Toronto
Fundersnot available
KeywordsVoxelFluid-attenuated inversion recoveryPartial volumeEnhanced Data Rates for GSM EvolutionWhite matterArtificial intelligencePattern recognition (psychology)Nuclear medicineBiomedical engineeringMagnetic resonance imagingComputer scienceMathematicsMedicineRadiology

Abstract

fetched live from OpenAlex

Through the combination of intensity and fuzzy edge strength measures, a new partial volume averaging (PVA) quantification technique for FLAIR MRI with white matter lesions (WML) is developed. It is focused on an edge-based approach, which "probes" for PVA voxels via a global estimate for the change in the proportion of tissues α'. This estimate is refined according to a probabilistic threshold, and the result is decoded to find the proportion of tissues fraction α - the percentage of one tissue found in a mixture voxel. The results from several images are shown illustrating how the technique may be used to segment PVA and pure tissue classes. The result is a non-model based approach to the detection and quantification of PVA.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.879
Threshold uncertainty score0.816

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.266
Teacher spread0.256 · 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 designSimulation or modeling
Domainnot available
GenreMethods

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

Citations3
Published2010
Admission routes1
Has abstractyes

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