3D Shape Estimation Of Tendon-Driven Catheters Using Ultrasound Imaging
Bibliographic record
Abstract
Active cable/tendon-driven catheters are becoming an established part of the minimally invasive surgical procedures. Therefore, there has been growing interest in literature in estimating the shape of their distal end especially using clinical ultrasound (US) imaging systems. The purpose of this thesis is to use a B-mode US imaging system to design time-efficient, accurate and robust algorithm for 3D shape estimation of tendon-driven catheters. Kalman filter (KF), Adaptive Kalman filter (AKF) and Particle filter (PF) algorithms were developed for this purpose. First, they were applied to a series of simulated US B-mode images where AKF provided the best estimate (error: 0.2 ± 0.1 mm). Second, they were applied to a series of experimentally obtained US B-mode images. Calibration procedures were carried out to calibrate these US images in the experiment’s workspace. The PF was shown to provide the best 3D shape estimate (error: 8.6 ± 0.1 mm). However, since almost the same accuracy could be achieved with AKF in ten times less computational time, AKF was concluded to be the best method, in terms of accuracy and efficiency, to estimate the 3D shape of tendon-driven catheters.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".