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Record W2131584519 · doi:10.1109/nssmic.2003.1352582

Catheter reconstruction from CT images

2004· article· en· W2131584519 on OpenAlexaff
D. Tubic, Luc Beaulieu

Bibliographic record

Venue2003 IEEE Nuclear Science Symposium. Conference Record (IEEE Cat. No.03CH37515) · 2004
Typearticle
Languageen
FieldEngineering
TopicAutomated Road and Building Extraction
Canadian institutionsCentre hospitalier universitaire de Québec
Fundersnot available
KeywordsIterative reconstructionComputer visionArtificial intelligenceCurvilinear coordinatesComputer sciencePlane (geometry)CatheterNoise (video)AlgorithmImage (mathematics)MathematicsRadiologyMedicine

Abstract

fetched live from OpenAlex

We present a novel procedure aimed at unsupervised catheter reconstruction from CT images. The proposed procedure is performed in two steps: detection of catheters on CT images and 3D reconstruction. In the first step catheters are detected as curvilinear structures where the first derivative in the direction of the normal vanishes. In the second step, geometric constraints on the shape of catheters are used to reconstruct catheters and to eliminate noise yielding from imperfect detection. Multiple solutions are removed by a combinatorial optimization algorithm. The algorithm can be used for both prostate catheters (approximately perpendicular to image plane) and breast catheters nearly parallel to image plane. The algorithm is automatic, without human interaction involved. Results obtained by testing the algorithm on clinical data are presented.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.002

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 head0.213
Teacher spread0.203 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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
Published2004
Admission routes1
Has abstractyes

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