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Record W2998268248 · doi:10.29007/w172

Shadow Peak: Accurate Real-time Bone Segmentation for Ultrasound and Developmental Dysplasia of the Hip

2019· article· en· W2998268248 on OpenAlexaff
Prashant Pandey, Niamul Quader, Kishore Mulpuri, Pierre Guy, Rafeef Garbi, Antony J. Hodgson

Bibliographic record

VenueEPiC series in health sciences · 2019
Typearticle
Languageen
FieldMedicine
TopicHip disorders and treatments
Canadian institutionsBC Children's HospitalUniversity of British Columbia
Fundersnot available
KeywordsSegmentationShadow (psychology)Computer scienceArtificial intelligenceImage segmentationFeature (linguistics)Computer visionPattern recognition (psychology)

Abstract

fetched live from OpenAlex

Confidence-weighted structured phase symmetry (CSPS) is a state-of-the-art bone seg- mentation technique for ultrasound (US), which has been recently proposed for automatic diagnosis for developmental dysplasia of the hip (DDH). However, CSPS relies on complex image phase feature analysis which is computationally expensive, and in our preliminary tests we have found it to be sometimes inaccurate. We evaluate a simpler alternative segmentation technique which we previously published, called Shadow Peak (SP), which uses intensity analysis to perform fast and accurate US bone segmentation. On average, SP segmentation ran 15 times faster for 2D US images, when tested on 15 hip images of pediatric patients. Furthermore, SP improves the segmentation F-score to 94%, compared to 72% when using CSPS segmentation.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.182

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 head0.024
GPT teacher head0.323
Teacher spread0.299 · 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 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

Citations5
Published2019
Admission routes1
Has abstractyes

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