An algorithm for automatic needle localization in ultrasound‐guided breast biopsies
Bibliographic record
Abstract
An algorithm was developed in order to reduce operator dependence in ultrasound-guided breast biopsy, by automatically locating the needle in the ultrasound image, and displaying its location on the image for the user. Ultrasound images of a typical breast biopsy needle inserted in a tissue-mimicking agar were obtained to test the algorithm. The resulting images were examined by a group of observers who recorded the values of the angle, intercept and tip coordinates of the needle in the image, and inter- and intra-observer variability studies were performed on the results. The results of the algorithm segmentation were compared to the values recorded by the observers, and physical measurements recorded at the time the images were acquired. The algorithm segmentation was precise enough to successfully (when considering angle and tip segmentation) target 90% of tumors of 4.5 mm in diameter situated at the center of the image.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".