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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".