Needle Segmentation For Real-time Guidance of Minimally Invasive Procedures Using Handheld 2D Ultrasound Systems
Bibliographic record
Abstract
Background Accurate needle placement is crucial during minimally invasive procedures such as biopsies, regional anesthesia, and fluid aspiration. 2D Ultrasound is widely used for needle guidance during such procedures, however, it has a limited field-of-view and poor needle visibility for steep insertion angles. Methods In this work, we propose a novel machine learning (ML)-based method for real-time needle segmentation in handheld 2D ultrasound systems. The proposed method involves a fast and simple annotation technique allowing for the labeling of large datasets. It then utilizes the U-Net architecture which is modified to allow for easy integration into a handheld ultrasound system. Two datasets were used in this work, one consisting of B-mode ultrasound videos obtained from human tissue and the other consisting of videos and frames from chicken, porcine and bovine tissue. The model is trained on 1262 frames and evaluated on 209 frames. Results This approach achieves an Intersection Over Union (IoU) of 0.75 and a dice coefficient of 0.851 on frames obtained from human tissue. The model is integrated into the processing pipeline of a portable ultrasound system and achieves an overall processing speed of about 8 frames per second. The proposed approach outperforms state-of-the-art methods for needle segmentation while achieving real-time integration. This work is a step forward towards real-time needle guidance using machine learning-based algorithms in handheld ultrasound systems.
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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".