MétaCan
Menu
Back to cohort
Record W2335563640 · doi:10.1186/1532-429x-18-s1-p165

Validation of a semi-automatic threshold-based approach for right ventricular endocardial border detection for volumetry

2016· article· en· W2335563640 on OpenAlexaff
Tamadhir Gazzaz, Paweena Chungsomprasong, Shi‐Joon Yoo, Mike Seed, Lars Grosse‐Wortmann

Bibliographic record

VenueJournal of Cardiovascular Magnetic Resonance · 2016
Typearticle
Languageen
FieldMedicine
TopicCardiovascular Disease and Adiposity
Canadian institutionsHospital for Sick Children
Fundersnot available
KeywordsAngiologyMedicineCardiologyInternal medicineData miningComputer science

Abstract

fetched live from OpenAlex

Cardiac magnetic resonance imaging (CMR) is the gold standard for evaluation of right ventricular (RV) volumes, function, and mass. The conventional postprocessing approach is based on ‘manual' delineation of the endocardium/blood border. The use of a threshold-based algorithm offers the theoretical advantage of a more inclusive definition of the RV myocardium, within a shorter postprocessing time. 60 children (mean age 13.9 ± 2.7 years) including 20 with structurally normal hearts and 40 after Tetralogy of Fallot (TOF) repair who underwent clinical CMR studies were included. The second group included 20 selected patients with good image quality and 20 unselected consecutive TOF studies (differences in performance of the volumetry approaches for patient subgroups are clinically important but reporting this information is beyond the constraints of an abstract). A cine short axis stack was acquired for ventricular volumetry. The RV endocardial border was identified by three methods: A) manual contouring with exclusion of RV trabeculations from the myocardium, B) including RV trabeculations, and C) semi-automatic thresholding (SAT) (Fig.1). The SAT method took less time than either variant of the conventional method (12.6 ± 2.2 min vs.17.5 ± 2.4 min by method A and 19.5 ± 2.9 min by method B,respectively, p < 0.001 for both). RV end-diastolic volume (EDV) measured by SAT was lower than that by conventional approaches (bias -11.4 ml/m in comparison with A and -6.9 ml/m with B). There was good agreement of SAT RV SV with both conventional methods (bias -0.4 ml/m and -1.8 ml/m ,respectively). Expectedly, RV mass by SAT was higher than by conventional methods (bias 13.2 gm/m and 8.8 gm/m ,respectively). The correlation between RV SV and MPA forward flow was moderate and comparable between all three methods (r 0.53-0.57). The bias between SAT derived SV and MPA forward flow was between that of methods A and B (bias 5.7 ml/m , 5.2 ml/m , and 6.9 ml/m for SAT, A, and B,respectively). SAT RV SV as well as EDV had higher interobserver variability compared to both conventional methods (bias -9.0 ml/m² vs. 4.2 ml/m and 0.83 ml/m ,respectively for SV and 5.4 ml/m² vs.1.43 ml/m and 2.81 ml/m , respectively for EDV). Interobserver reproducibility for RV mass was superior by SAT as compared to the conventional method A and B (bias -4.0 gm/m² vs.-0.35 gm/m² and -2.7 gm/m ,respectively). The semi-automated threshholding method for RV endocardial border detection is faster compared to the conventional methods. RV EDV by SAT are smaller and RV mass greater than by conventional approaches. Reproducibility for RV EDV was lower and that for RV mass was better by SAT. The accuracy compared to MPA flow measurements are comparable between all three approaches. SAT is an alternative postprocessing strategy with potential advantages for RV mass measurements. Operator training in a uniform contouring / threshholding approach remains a prerequisite for successful quantification of RV mass and volumes. Semi-automatic endocardial contour detection using a threshold algorithm . The epicardial contours are manually drawn.

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)0.005
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.010
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.008
GPT teacher head0.235
Teacher spread0.227 · 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 designObservational
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
Published2016
Admission routes1
Has abstractno

Explore more

Same venueJournal of Cardiovascular Magnetic ResonanceSame topicCardiovascular Disease and AdiposityFrench-language works237,207