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Record W2960161856 · doi:10.1109/ultsym.2019.8925710

Measuring Spinous Process Angle on Ultrasound Spine Images using the GVF Segmentation Method

2019· article· en· W2960161856 on OpenAlex
Honegye Zeng, Rui Zheng, Lawrence H. Le, Dean Ta

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMedical Imaging and Analysis
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsComputer visionArtificial intelligenceProcess (computing)Image segmentationSegmentationComputer science

Abstract

fetched live from OpenAlex

Spinous process angle (SPA) is an important parameter to evaluate the severity of scoliosis. However, the spinous process cannot be accurately automatic located due to the interference of the muscle layer. The objectives of this study are to apply gradient vector flow (GVF) snake model to segment spinous process (SP) on US transverse vertebral images and to illustrate and measure SPA on US coronal spine images. The snake method could detect SP tip position reliably in this study. Ten spinous process curves were identified on both radiographs and US images. The mean absolute difference (MAD) of SPAs obtained from the two modalities was 2.5±1.9°. It demonstrates the SPA measured from US images via GVF snake model is comparable with the results from the conventional radiographs.

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.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.397
Threshold uncertainty score0.278

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 head0.025
GPT teacher head0.293
Teacher spread0.268 · 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

Quick stats

Citations8
Published2019
Admission routes1
Has abstractyes

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