A real‐time biopsy needle segmentation technique using Hough Transform
Bibliographic record
Abstract
Real-time needle segmentation and tracking is very important in image-guided surgery, biopsy, and therapy. Due to its robustness to the addition of extraneous noise, the Hough Transform is one of the most powerful line-detection techniques nowadays and has been widely used in different areas. Unfortunately, its high computation needs often prevent it from being applied in real-time applications without the help of specially designed hardware. In order to solve this problem, a variety of fast implementation algorithms have been proposed. However, none of them can be performed in a real time on an affordable computer. In this paper, we describe a fast implementation of the Hough Transform based on coarse-fine search and the determination of the optimal image resolution. Compared to conventional techniques, our approach decreases the time for needle segmentation by an order of magnitude. Experiments with agar phantom and patient breast biopsy ultrasound (US) image sequences showed that our approach can segment the biopsy needle in real time (i.e., less than 33 ms) on an affordable PC computer without the help of specially designed hardware with the angular rms error of about 1 degrees and the position rms error of about 0.5 mm.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".