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Record W2109213053 · doi:10.5555/602099.602104

Direct surface extraction from 3D freehand ultrasound images

2002· article· en· W2109213053 on OpenAlex

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

VenuecIRcle (University of British Columbia) · 2002
Typearticle
Languageen
FieldComputer Science
TopicMedical Image Segmentation Techniques
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsVoxelArtificial intelligenceComputer sciencePixelComputer vision3D ultrasoundNoise (video)Data setSampling (signal processing)UltrasoundImage resolutionFeature extractionSurface (topology)Pattern recognition (psychology)Image (mathematics)MathematicsAcousticsGeometry

Abstract

fetched live from OpenAlex

Surface extraction from ultrasound data is challenging for a number of reasons, including noise and artifacts in the images and non-uniform data sampling. This thesis presents a new technique for the extraction of surfaces from freehand 3D ultrasound data. Most available 3D medical visualization methods fall into two categories: volume rendering and surface rendering. Surface rendering is chosen here because one of the long term goals of this thesis is explicit modelling of organs. Recent progress has been made in surface extraction for a range data or an unorganized data set, by using Radial Basis Functions (RBFs) to represent the whole space with a signed distance function. Instead of using geometric distance as in previous work, this thesis proposes to use pixel intensity directly as a distance function. A new implementation of a freehand 3D ultrasound acquisition system is also introduced in this thesis using a trinocular optical tracking system with light-emitting diodes (LEDs) attached to an ultrasound probe. To calibrate the transformation between the ultrasound image coordinate system and the LED coordinate system, an N-wire calibration phantom was designed. High accuracy is obtained by using multiple images to oversample the calibration points and reduce the level of error. To complete the calibration, geometry of the calibration phantom is measured using a pointing device that is also based on optical tracking. Once calibrated, the 3D freehand ultrasound system is used to scan an object. The images obtained, along with the measured positions, are the inputs of the RBF surface extraction algorithm. First an automatic segmentation method is used to trim extraneous data points to reduce computational demands. Then the data is interpolated by the RBFs, and a surface extracted along isovalued regions. Results using the direct surface extraction method with RBF are shown to successfully extract ultrasound surfaces from thepoint cloud. Surfaces of both phantom and human skin are shown with high fidelity of shape and details. " In summary, this research is the first to represent the set of semi-structured ultrasound pixel data as a single function. From this, we are able to extract realistic surfaces without first reconstructing the irregularly spaced pixels into a regular 3D voxel array. The main advantage of this new approach is to avoid any loss of information normally associated with reconstruction of voxel array.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.992
Threshold uncertainty score0.997

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 communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.011
GPT teacher head0.195
Teacher spread0.184 · 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