A Hybrid Approach to 3D Shape Estimation of Catheters Using Ultrasound Images
Bibliographic record
Abstract
Catheter-based cardiac interventions have substantially improved the efficacy of the treatment. However, catheter navigation still poses several challenges including its poor visualization and localisation. Therefore, various imaging methods have been used to localize the catheter and estimate its shape. The use of ultrasound imaging is superior to other modalities in many aspects, but suffers from poor spatial resolution. Hence, we present a hybrid approach involving deep learning and classical approach to 3D shape estimation of catheters. Our novel approach uses two stages. First, a UNet-3D model is proposed to estimate the catheter centroid in the ultrasound image. Then, an adaptive Kalman Filter (AKF) is used to fuse the points into the 3D world coordinate frame. Simulation studies, phantom and ex-vivo experimentation results demonstrate the robustness of the method to ultrasound noise and extreme configurations (sharp curves).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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".