Robust Deep Feature Ultrasound Image-Based Visual Servoing: Focus on Cardiac Examination
Bibliographic record
Abstract
This article introduces a robust deep feature ultrasound image-based visual servoing (UIBVS) technique for an ultrasound robot focusing on automatic cardiac examination. To this end, a convolutional neural network named ultrasound-cardiac-feature-net (UCF-Net) is developed, which is trained in a supervised manner to process ultrasound images and generate a set of six image features referred to as deep ultrasound image features. To enhance the robustness of UCF-Net against the variables that affect the ultrasound image quality, such as interaction normal force, scan depth, dynamic range, power, and gain, several datasets with different sets of parameters are gathered for training. Deep ultrasound image features enable an eye-in-hand robot to interact with the human body through UIBVS. To implement UIBVS, a filtered integral quasi-super-twisting algorithm (FIQSTA) is synthesized as the primary controller. Interaction force control is also considered within a hybrid vision/force control framework, providing compliance with the body and increasing the safety of the interaction. The proof of the robustness and stability of FIQSTA is also investigated. Experimental results on a cardiac phantom for four main views, i.e., parasternal short axis, parasternal long axis, subcostal, and apical four chambers views, and a trajectory passing through the main views demonstrate the feasibility of the proposed method for cardiac examination and the superior performance of the main controller to other well-known methods, including proportional (P) controller, sliding mode controller, super-twisting algorithm (STA), and integral quasi-STA.
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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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".