Mingyue Ding

About

Mingyue Ding’s research bridges robotics, medical imaging, and artificial intelligence, with a focus on developing intelligent systems for healthcare and autonomous navigation. A major contribution is in prostate brachytherapy, where Ding pioneered 3D transrectal ultrasound (TRUS)-guided robotic systems for dynamic, intraoperative seed placement. These works, including a highly cited 2005 paper (40 citations), introduced robot-assisted needle segmentation, tracking, and real-time planning, significantly improving precision in cancer treatment. In robotics, Ding advanced path planning by applying quantum genetic algorithms—leveraging quantum superposition to encode routes—and proposed novel roadmap methods using grid division and level-set techniques for reusable navigation in dynamic environments. More recently, Ding has contributed to computer vision with a 2023 paper on monocular depth estimation, reinforcing local structure perception for applications in robot navigation and autonomous driving. With over 60 citations across key publications, Ding’s work demonstrates sustained impact in both clinical and autonomous systems, reflecting a career dedicated to integrating computational intelligence with practical robotic solutions.

Research Focus

Key Achievements

Top Papers

1. 1
   3D TRUS Guided Robot Assisted Prostate Brachytherapy

   40 citations · 2005
2. 2
   Quantum Genetic Algorithm for Mobile Robot Path Planning

   9 citations · 2010
3. 3
   Dynamic Intraoperative Prostate Brachytherapy Using 3D TRUS Guidance with Robot Assistance

   6 citations · 2005
4. 4
   Reinforcing Local Structure Perception for Monocular Depth Estimation

   4 citations · 2023
5. 5
   A New Roadmap Constitution Method Based on Dividing Grid and Level-Set

   2 citations · 2010

Key Collaborators

Contact & Links