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Record W2048384871 · doi:10.1117/12.594803

Deformation analysis of Hoffa's fat pad from CT images of knee flexion and extension

2005· article· en· W2048384871 on OpenAlexafffund
Ghassan Hamarneh, Vincent H. Chu, Marcelo Bordalo Rodrigues, Mark E. Schweitzer

Bibliographic record

VenueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2005
Typearticle
Languageen
FieldComputer Science
TopicHuman Pose and Action Recognition
Canadian institutionsSimon Fraser University
FundersU.S. National Library of MedicineNatural Sciences and Engineering Research Council of CanadaSimon Fraser UniversityUniversities Space Research AssociationWestern Canada Research Grid
KeywordsFat padSegmentationComputer scienceDisplacement (psychology)Knee flexionArtificial intelligenceComputer visionAnatomyBiomedical engineeringMathematicsMedicineOrthodontics

Abstract

fetched live from OpenAlex

Recent advances in medicine conjecture that certain body fat may have mechanical function in addition to its classical role of energy storage. In particular we aim to analyze if the intra-articular fat pad of Hoffa is merely a space holder or if it changes shape to provide cushioning for the knee bones. Towards this goal, 3D CT images of real knees, as well as a skeletal knee model with fat simulating Hoffa's pad, were acquired in both extension and flexion. Image segmentation was performed to automatically extract the real and simulated fat regions from the extension and flexion images. Utilizing the segmentation results as binary masks, we performed automatic multi-resolution image registration of the fat pad between flexed and extended knee positions. The resulting displacement fields from flexion-extension registration are examined and used to calculate local fat volume changes thus providing insight into shape changes that may have a mechanical component.

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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.304
Threshold uncertainty score0.646

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.011
GPT teacher head0.228
Teacher spread0.217 · 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

Citations12
Published2005
Admission routes2
Has abstractyes

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Same venueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIESame topicHuman Pose and Action RecognitionFrench-language works237,207