A Robot-Assisted Optical Coherence Tomography (OCT) System for Inspecting Inner Wall Defects in Nonspherical Lumens
Bibliographic record
Abstract
Inspecting defects in nonspherical lumens is challenging due to occlusion and depth variation, hindering complete point cloud data reconstruction. This article introduces a robot-assisted optical coherence tomography (OCT) system for conducting reconstruction and inner wall defects detection tasks in nonspherical lumens, leveraging robotic arm’s flexibility. Unlike image sensors, the developed OCT probe collects data via rotating scanning, complicating hand–eye calibration due to the lack of calibration features and coordinate drift in rotating probe. To address this, a decoupled hand–eye calibration method was proposed, utilizing iterative sphere-center fitting to improve accuracy in limited features and address coordinate drift problem. Moreover, OCT-acquired point clouds often include noise, artifacts, and exhibit anisotropy, complicating the detection of subtle defects. To overcome this challenge, we proposed a geometry-aware detection method based on local normal estimation to inspect defects in nonspherical lumens. Experimental results demonstrated a 55.2% improvement in hand–eye calibration with the proposed method, achieving a sphere fitting error of$12.4~{\mu }$m compared to$27.7~{\mu }$m in the traditional method. The method showed an average accuracy of 87.9% in detecting micrometer-level lumen defects, outperforming the state-of-the-art (SOTA) anomaly detection method (3D-ST), which achieved an average accuracy of 62.2%.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".