A Two-Step Method for Real-Time Needle Tracking in MRI Images
Bibliographic record
Abstract
Needle segmentation is vital in MRI-guided robotic brain and neural surgeries as it provides essential navigation information. Tracking the needle in real-time MRI scanning images presents significant challenges. In this research, we proposed a two-step method to implement precise needle segmentation in MRI images, specifically designed for time-sensitive applications. The first step employed a Convolutional Neural Network (CNN) based model to trim the original image, extracting a smaller fixed-size cropping that contains all relevant needle features. In the second step, a segmentation model integrating U-Net with a ResNet module and attention mechanism was utilized. This model processed the cropped image and generated accurate needle segmentations, along with needle axis orientations. Our proposed method achieved a mean tip location error measured at 1.407 pixels, while the mean axial direction error was 1.353 degrees. These results highlighted the method's effectiveness and potential contributions to real-time medical image analysis, as well as its potential to support diverse interventional procedures and clinical applications.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".