Papers
12
Total Citations
485
H-Index
8
About
Zhen-Liang Ni is a computer vision researcher whose work sits at the intersection of deep learning and surgical robotics, with a particular focus on the segmentation and tracking of medical instruments in minimally invasive procedures. His most influential contribution, RAUNet (Residual Attention U-Net), addresses the precise semantic segmentation of cataract surgical instruments and has garnered over 126 citations, establishing him as a notable voice in medical image analysis. Building on this foundation, Ni developed RASNet, a Refined Attention Segmentation Network tailored for tracking surgical tools in robotic video streams, and later introduced lightweight, real-time architectures capable of meeting the computational demands of live surgical environments. His participation in the ROBUST-MIS 2019 challenge — a landmark multi-institution validation study with nearly 90 citations — underscores his engagement with community-driven benchmarking efforts. More recently, his work has expanded into guidewire segmentation for endovascular interventions and gesture recognition in percutaneous coronary procedures, reflecting a broadening vision for intelligent surgical assistance. Across his portfolio, Ni's research consistently pursues the dual goals of accuracy and clinical deployability, making meaningful contributions to safer, more autonomous robotic surgery.
Research Focus
Key Achievements
Top Papers
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- 6Robust Medical Instrument Segmentation Challenge 201933 citations · 2020
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