<title>Three-dimensional ultrasound imaging</title>
Bibliographic record
Abstract
The last two decades have witnessed unprecedented developments of imaging systems making use of 3D visualization. These new technologies have revolutionized diagnostic radiology, by providing information about the interior of the human body never before available. Ultrasound imaging is an important cost-effective technique used routinely in the management of a number of diseases. However, technical improvements are needed before its full potential is realized, particularly in applications involving minimally invasive therapy or surgery. 2D viewing of 3D anatomy, using conventional ultrasound, limits our ability to quantify and visualize the anatomy and guide therapy. This occurs because the use of 2D ultrasound requires that the diagnostician integrate multiple images in his mind. This practice is inefficient, and may lead to variability and incorrect diagnoses. Also, the 2D ultrasound image represents a thin plane at an arbitrary angle in the body. It is difficult to localize the image plane, and reproduce it at a later time. Over the past 2 decades, investigators have addressed these limitations by developing 3D ultrasound techniques. In this paper we describe our developments of 3D ultrasound techniques for imaging organs such as the prostate, breast, and kidney. To produce a 3D image, the ultrasound transducer is scanned mechanically or using a free-hand technique. The images are digitized and then reconstructed into a 3D image, which can be viewed and manipulated interactively. In addition, the user can segment the organ and measure its volume manually or using semi-automatic techniques. In this paper we describe the use of 3D ultrasound for diagnosis, image-guided therapy and quantifying organ volume. Examples will be given for imaging various organs, such as the prostate, carotid arteries, and breast, and for the use in 3D ultrasound-guided brachytherapy. In addition, we describe 3D segmentation methods that can be used for analysis of the volume of the prostate and carotid vessel lumen using 3D ultrasound images. The segmentation techniques applied to 3D ultrasound images has been shown to be less variable than manual segmentation techniques and of value in both 3D ultrasound-guided prostate brachytherapy and in the assessment of carotid plaque progression/regression.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.563 | 0.507 |
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".