MétaCan
Menu
Back to cohort
Record W2049104356 · doi:10.1118/1.4814282

SU‐E‐J‐70: Intra and Intermodality Validation of Registration Algorithms On a Deformable Phantom

2013· article· en· W2049104356 on OpenAlexaff
S D. Vincent, Louis Archambault

Bibliographic record

VenueMedical Physics · 2013
Typearticle
Languageen
FieldEngineering
TopicMedical Imaging and Analysis
Canadian institutionsHôtel-Dieu de Québec
Fundersnot available
KeywordsImaging phantomImage registrationMetric (unit)Computer scienceDeformation (meteorology)Artificial intelligenceComputer visionMedical imagingTranslation (biology)AlgorithmMutual informationNuclear medicineMathematicsImage (mathematics)MedicinePhysics

Abstract

fetched live from OpenAlex

Purpose: Clinical use of deformable image registration algorithms (DRA) requires a thorough preliminary study of their capacity and performance. To rigorously quantify the registrations, images showing deformation of precise amplitude must be used. Deformations representative of the average anatomical variations possibly encountered during radiation treatments were used for DRA evaluation. Methods: A three‐dimensional anthropomorphic phantom representing the main pelvic structures (prostate, bladder, rectum, femoral heads) was created. Three types of deformation were studied: translation (prostate), volume variation (bladder) and deformation of contours without volume change (prostate). The design of the phantom allows a precise knowledge of deformation amplitudes. Each pairs of initial and deformed images were scanned on both CT and CBCT. This way an image database offering the same images on both modalities was available for intra and intermodality validation. The algorithm validated was an open‐source software (Elastix) that offers a large control of the registration parameters. Results: Deformation of 5 mm on the contour, translation of 0.5, 1, 2 cm and volume variation of 50, 100, 200 ml were scanned on both modalities. Registration with basic Mutual information metric succeed between 98% and 71%, these results representing the fraction of the deformation registered with success on its initial image. The worst cases were observed when a CT is registered onto a CBCT. By combining the Mutual information registration metric with manually delineated points (corresponding point metric) the worst cases (71 to 79%) were greaty improved (88 to 96%). Conclusion: By optimizing the deformation parameters in a controlled setting (i.e. an anthropomorphic phantom) we could achieve better DRA efficiency. Combining multiple registration metric lead to registration of superior quality. Deformable vector field shows that many areas free from actual deformation were altered during registration which indicate that deformation parameters optimized to improve registration of organ contours are not well suited for dose deformation.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.984
Threshold uncertainty score0.271

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 communication0.0000.000
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 head0.012
GPT teacher head0.242
Teacher spread0.230 · 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 designOther design
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

Citations0
Published2013
Admission routes1
Has abstractyes

Explore more

Same venueMedical PhysicsSame topicMedical Imaging and AnalysisFrench-language works237,207