Xiao-Yun Zhou

About

Xiao-Yun Zhou is a pioneering researcher at the intersection of artificial intelligence, medical image analysis, and robot-assisted surgery. Their work spans deep learning-based biomedical image segmentation, surgical navigation, and autonomous robotic systems for minimally invasive procedures. Zhou's most cited contribution, "Application of Artificial Intelligence in Surgery" (2020, 171 citations), has become an essential reference for researchers exploring AI's transformative role in the operating room. Their influential work on normalization strategies in U-Net training (2019, 107 citations) addressed a critical challenge in 2D biomedical semantic segmentation, improving the reliability of deep convolutional neural networks for surgical vision tasks. A recurring theme in Zhou's research is enhancing intraoperative navigation for complex vascular procedures, particularly Fenestrated Endovascular Aortic Repair (FEVAR), where they developed innovative methods for 3D shape instantiation and path planning from limited 2D fluoroscopic data. Their ACNN architecture further advanced full-resolution medical image segmentation, while separate work on cardiac radiofrequency catheter ablation demonstrated versatility across surgical domains. With a cumulative citation count exceeding 400, Zhou's contributions have meaningfully advanced the safety, precision, and intelligence of robot-assisted surgery, making their body of work essential reading for students in medical robotics and computational medicine.

Research Focus

Key Achievements

Top Papers

1. 1
   Application of artificial intelligence in surgery

   171 citations · 2020
2. 2
   Normalization in Training U-Net for 2-D Biomedical Semantic Segmentation

   107 citations · 2019
3. 3
   Towards Automatic 3D Shape Instantiation for Deployed Stent Grafts: 2D Multiple-class and Class-imbalance Marker Segmentation with Equally-weighted Focal U-Net

   27 citations · 2018
4. 4
   ACNN: a Full Resolution DCNN for Medical Image Segmentation

   24 citations · 2020
5. 5
   Towards 3D Path Planning from a Single 2D Fluoroscopic Image for Robot Assisted Fenestrated Endovascular Aortic Repair

   22 citations · 2019
6. 6
   An efficient cardiac mapping strategy for radiofrequency catheter ablation with active learning

   16 citations · 2017
7. 7
   Path planning for robot-enhanced cardiac Radiofrequency Catheter Ablation

   13 citations · 2016
8. 8
   Real-time Surgical Environment Enhancement for Robot-Assisted Minimally Invasive Surgery Based on Super-Resolution

   12 citations · 2021
9. 9
   Focal FCN: Towards Small Object Segmentation with Limited Training Data.

   12 citations · 2017
10. 10
    IEEE Robotics and Automation Letters publication information

    11 citations · 2019

Key Collaborators

Contact & Links