Robotic tracking of a resection cavity using a low cost bench-top robotic arm and electromagnetics
Bibliographic record
Abstract
<strong>INTRODUCTION:</strong> Roughly 40% of breast cancer patients are required to undergo corrective surgery after tumour resection via breast-conserving surgery (BCS). Sweeping of the cavity, resulting from the tumour resection, by spectroscopy and ultrasound imaging is emerging as a potential solution for identifying leftover cancer. However, the use of imaging modalities in the cavity is challenging as breast tissue is soft, malleable, and moves frequently. This paper presents and verifies an approach for tracking the relative motion of a resection cavity with a robotic arm. <strong>METHODS: </strong>We use electromagnetic tracking and a low cost 6-axis robotic arm to track a simulated resection cavity. We embed an electromagnetic sensor in a 3D printed retractor that is designed to hold the cavity open. An open-source module in 3D Slicer is then used to detect cavity motion from the retractor and command the robotic arm to follow the relative movement while incorporating motion planning to prevent collisions and unsafe actions. To assess this approach we move the retractor to 36 positions in the robotic arm workspace and measure the latency between when a command is published to the robotic arm and when it begins to move to this position. In addition, we attach a camera to the end-effector (EE) of the robotic arm to determine when the robotic arm has successfully tracked the cavity by checking if it is visible in the center of the camera frame. <strong>RESULTS: </strong>The latency was recorded to be 832.1 milliseconds on average with 132.6 standard deviation. We can also successfully track the motion of the cavity in almost every test position. <strong>CONCLUSIONS: </strong>These results suggest that tracking of the breast cavity using EM tracking and robotics is feasible. Future work focusing on the integration with spectroscopy and cavity servoing.
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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".