126 citations · 734 across the 65 of their papers we have counts for
8 papers · 1 filter
TAToo: Vision-based Joint Tracking of Anatomy and Tool for Skull-base Surgery
Zhaoshuo Li, Hongchao Shu, Ruixing Liang +5
Purpose: Tracking the 3D motion of the surgical tool and the patient anatomy is a fundamental requirement for computer-assisted skull-base surgery. The estimated motion can be used…
Rethinking Causality-driven Robot Tool Segmentation with Temporal Constraints
Hao Ding, Jie Ying Wu, Zhaoshuo Li +1
Purpose: Vision-based robot tool segmentation plays a fundamental role in surgical robots and downstream tasks. CaRTS, based on a complementary causal model, has shown promising pe…
Twin-S: A Digital Twin for Skull-base Surgery
Hongchao Shu, Ruixing Liang, Zhaoshuo Li +9
Purpose: Digital twins are virtual interactive models of the real world, exhibiting identical behavior and properties. In surgical applications, computational analysis from digital…
Context-Enhanced Stereo Transformer
Weiyu Guo, Zhaoshuo Li, Yongkui Yang +5
Stereo depth estimation is of great interest for computer vision research. However, existing methods struggles to generalize and predict reliably in hazardous regions, such as larg…
SyntheX: Scaling Up Learning-based X-ray Image Analysis Through In Silico Experiments
Cong Gao, Benjamin D. Killeen, Yicheng Hu +4
Artificial intelligence (AI) now enables automated interpretation of medical images for clinical use. However, AI's potential use for interventional images (versus those involved i…
CaRTS: Causality-driven Robot Tool Segmentation from Vision and Kinematics Data
Hao Ding, Jintan Zhang, Peter Kazanzides +2
Vision-based segmentation of the robotic tool during robot-assisted surgery enables downstream applications, such as augmented reality feedback, while allowing for inaccuracies in…
SAGE: SLAM with Appearance and Geometry Prior for Endoscopy
Xingtong Liu, Zhaoshuo Li, Masaru Ishii +3
In endoscopy, many applications (e.g., surgical navigation) would benefit from a real-time method that can simultaneously track the endoscope and reconstruct the dense 3D geometry…
Mapping DNN Embedding Manifolds for Network Generalization Prediction
Molly O'Brien, Julia Bukowski, Mathias Unberath +2
Understanding Deep Neural Network (DNN) performance in changing conditions is essential for deploying DNNs in safety critical applications with unconstrained environments, e.g., pe…