Direct surface extraction from 3D freehand ultrasound images
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".