Search Results for author:
Found 5 papers, 0 papers with code
Medical Instrument Segmentation in 3D US by Hybrid Constrained Semi-Supervised Learning
These results are better than the state-of-the-art SSL methods and the inference time is comparable to the supervised approaches.
Weakly-supervised Learning For Catheter Segmentation in 3D Frustum Ultrasound
Accurate and efficient catheter segmentation in 3D ultrasound (US) is essential for cardiac intervention.
Medical Instrument Detection in Ultrasound-Guided Interventions: A Review
Medical instrument detection is essential for computer-assisted interventions since it would facilitate the surgeons to find the instrument efficiently with a better interpretation, which leads to a better outcome.
Deep Q-Network-Driven Catheter Segmentation in 3D US by Hybrid Constrained Semi-Supervised Learning and Dual-UNet
To train the Dual-UNet with limited labeled images and leverage information of unlabeled images, we propose a novel semi-supervised scheme, which exploits unlabeled images based on hybrid constraints from predictions.
Improving Catheter Segmentation & Localization in 3D Cardiac Ultrasound Using Direction-Fused FCN
Fast and accurate catheter detection in cardiac catheterization using harmless 3D ultrasound (US) can improve the efficiency and outcome of the intervention.
