Estimating the Joint Angles of an Articulated Microrobotic Instrument Using Optical Coherence Tomography
Bibliographic record
Abstract
Pose estimation of surgical tools is necessary for controlling and manipulating the tool in confined surgical regions for minimally invasive surgeries. Several studies have explored the application of robot assisted surgeries which require an imaging system to track the tool and navigate it with high accuracy and precision. Optical Coherence Tomog-raphy (OCT) is an emerging volumetric imaging modality in minimally invasive robot assisted surgery. We present a marker-based computer vision algorithm to estimate the wrist and finger joint angles of a neurosurgical gripper tool from an OCT volume. The tool's joint angles lie on two perpendicular planes. Markers of 1 mm diameter are placed on the tool and then the gripper is imaged under the OCT system. The raw volumetric images are pre-processed by downsampling and applying thresholds. The markers in the OCT volume are then detected using 3D template matching. False positive detections are algorithmically omitted by evaluating the relative distances between the markers. Finally, the positions of these detected markers are used to estimate the joint angles. Our approach yields an average error of 2.20 and a standard deviation of 3.4° for the wrist joint$(\theta_{1})$. For the finger joint$(\theta_{2})$, it yields an average error of 2° and a standard deviation of 1.5°• The estimations are provided within 0.5 seconds on a PC with a 16 core CPU.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".