Projection-based needle segmentation in 3D ultrasound images
Bibliographic record
Abstract
Needles are used extensively in interventional procedures such as biopsy and brachytherapy. To deliver radioactive seeds to pre-planned positions or sample lesions from the region that may contain cancer cells, the 3D position of the needle must be determined accurately and quickly. Three-dimensional ultrasound (US) image guidance is an efficient technique used to perform this task. In this paper, we describe the development of a projection-based needle segmentation method comprising three steps. First, the 3D image is projected along an initial direction perpendicular to the approximate needle direction determined from the 3D imaging system. The needle is then segmented in a projected 2D image. Using the projection direction and the detected 2D needle direction, a plane containing the needle--called the needle plane--is determined. Secondly, the 3D image is re-projected in the direction perpendicular to the normal of the needle plane and step 1 is repeated. If the needle direction in the projected 2D image is horizontal, the needle plane is correct; otherwise, steps 1 and 2 are repeated until a correct needle plane is found. Thirdly, the 3D image is projected along the normal direction of the needle plane and the needle endpoints in the projected 2D image are determined. Using the relationship between the 3D projection and the 3D volume coordinate systems, the coordinates of the endpoints of the needle in the 3D US coordinate system are determined. Experiments with agar and turkey phantom 3D US images demonstrated that our method could segment the needle from 3D US images with an average accuracy of 0.7 mm in position and 1.2 degrees in orientation with a speed of 13 fps on a 1.3-GHz PC. In addition, experiments illustrated that our method is robust to variations in the initial estimated needle direction, the size of the cropped volume, and the ray-casting transfer function parameters used in pre-processing.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".