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Record W2036938784 · doi:10.1109/icpr.2010.824

An Improved Fluid Vector Flow for Cavity Segmentation in Chest Radiographs

2010· article· en· W2036938784 on OpenAlexaff
Tao Xu, Irene Cheng, Mrinal Mandal

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicMedical Image Segmentation Techniques
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsSegmentationVector flowParametric statisticsComputer scienceActive contour modelArtificial intelligenceImage segmentationRadiographyPoint distribution modelActive shape modelComputer visionEdge detectionPattern recognition (psychology)MathematicsRadiologyImage processingMedicineImage (mathematics)

Abstract

fetched live from OpenAlex

Fluid vector flow (FVF) is a recently developed edge-based parametric active contour model for segmentation. By keeping its merits of large capture range and ability to handle acute concave shapes, we improved the model from two aspects: edge leakage and control point selection. Experimental results of cavity segmentation in chest radiographs show that the proposed method provides at least 8% improvement over the original FVF method.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.280
Threshold uncertainty score0.368

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)0.0000.000

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 head0.292
Teacher spread0.281 · 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
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

Citations8
Published2010
Admission routes1
Has abstractyes

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