An accessible six-axis testbed for image-guided robotics research
Bibliographic record
Abstract
<strong>PURPOSE</strong>: Cancer can recur after tumor resection surgery if tumor tissue is missed and left behind. We hypothesize that intraoperative robotic imaging could be used to inspect the surgical cavity and localize residual cancer tissue. This technique has the potential to improve the success rate of tumor resection surgery. Towards this, we propose and evaluate a benchtop testbed for robotic manipulation of an optical imaging probe. We use low-cost hardware and open-source software to construct the testbed and describe the implementation so that it can be easily adopted to support similar research. <strong>METHODS</strong>: We implemented a reusable, open-source module in 3D Slicer for reading position coordinates and motion planning with an inexpensive 6-axis robotic arm in Robot Operating System (ROS). For demonstration, a custom end-effector was used to fix an optical probe to the robot. The accuracy of the testbed was assessed using a 3D-printed phantom with 16 target points. We used the testbed to automatically scan the phantom and measured the positioning accuracy of the robot. <strong>RESULTS</strong>: The testbed had an average positional accuracy of <strong>3.59 ± 1.4</strong> mm and successfully navigated to the majority of target points. <strong>CONCLUSIONS</strong>: While the current positional accuracy requires further refinement for clinical applications, this open-source benchtop system provides a foundational step towards developing benchtop image-guided tumor inspection systems using low-cost robotics. Future work will explore the application of this test bed within breast conserving surgery along with increasing the positional accuracy of the system.
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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 | 0.001 |
| 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".