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Record W2537371119 · doi:10.1109/tic-sth.2009.5444517

Deformable modeling of human liver with contact surface

2009· article· en· W2537371119 on OpenAlexafffund
Adil Al‐Mayah, Joanne Moseley, Kristy K. Brock

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicMedical Image Segmentation Techniques
Canadian institutionsPrincess Margaret Cancer Centre
FundersTerry Fox FoundationNational Cancer InstituteCancer Care OntarioFoundation for the National Institutes of Health
KeywordsDisplacement (psychology)Surface (topology)Body surfaceBreathingFinite element methodSpleenBiomedical engineeringMaterials scienceAnatomyComputer visionMedicinePhysicsComputer scienceGeometryMathematicsInternal medicine

Abstract

fetched live from OpenAlex

Patient specific 3D finite element models have been developed using 4DCT (3D + time) image data for 5 liver cancer patients. Each model consists of the liver, tumors, left and right kidneys, stomach, spleen and body. Breathing motion of the liver, spleen and body is found and applied as displacement boundary conditions in the model. Sliding of the liver relative to the surrounding tissues is modeled using frictionless contact surfaces. Landmarks representing vessels bifurcation inside the liver are used for the model accuracy test. The goal of the study is to examine the effect of contact surface model on the performance of the deformable image registration of the liver. The accuracy of the model is improved by applying contact surface. Substantial displacement differences are observed between models with and without contact surface of the liver. The largest difference is in the Superior-Inferior (SI) followed by Anterior-Posterior (AP).

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: none
Teacher disagreement score0.715
Threshold uncertainty score0.155

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 head0.024
GPT teacher head0.277
Teacher spread0.253 · 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

Citations6
Published2009
Admission routes2
Has abstractyes

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