14 citations · 40 across the 5 of their papers we have counts for
5 papers
Medical Instrument Segmentation in 3D US by Hybrid Constrained Semi-Supervised Learning
Hongxu Yang, Caifeng Shan, R. Arthur Bouwman +3
Medical instrument segmentation in 3D ultrasound is essential for image-guided intervention. However, to train a successful deep neural network for instrument segmentation, a large…
Weakly-supervised Learning For Catheter Segmentation in 3D Frustum Ultrasound
Hongxu Yang, Caifeng Shan, Alexander F. Kolen +1
Accurate and efficient catheter segmentation in 3D ultrasound (US) is essential for cardiac intervention. Currently, the state-of-the-art segmentation algorithms are based on convo…
Medical Instrument Detection in Ultrasound-Guided Interventions: A Review
Hongxu Yang, Caifeng Shan, Alexander F. Kolen +1
Medical instrument detection is essential for computer-assisted interventions since it would facilitate the surgeons to find the instrument efficiently with a better interpretation…
Deep Q-Network-Driven Catheter Segmentation in 3D US by Hybrid Constrained Semi-Supervised Learning and Dual-UNet
Hongxu Yang, Caifeng Shan, Alexander F. Kolen +1
Catheter segmentation in 3D ultrasound is important for computer-assisted cardiac intervention. However, a large amount of labeled images are required to train a successful deep co…
Improving Catheter Segmentation & Localization in 3D Cardiac Ultrasound Using Direction-Fused FCN
Hongxu Yang, Caifeng Shan, Alexander F. Kolen +1
Fast and accurate catheter detection in cardiac catheterization using harmless 3D ultrasound (US) can improve the efficiency and outcome of the intervention. However, the low image…