Jianwen Cheng
Papers
1
Total Citations
2
H-Index
1
About
Jianwen Cheng is a researcher advancing the field of surgical robotics, with a primary focus on computer-assisted orthopedic procedures. Their work centers on developing automated methods for 3D image analysis and feature extraction, particularly for complex anatomical structures like the pelvis. Cheng's most notable contribution is the 2025 paper "A Method for Automatic Feature Points Extraction of Pelvic Surface Based on PointMLP_RegNet," which addresses a critical challenge in robot-assisted pelvic fracture reduction surgery. This work proposes a deep learning approach using PointMLP and RegNet architectures to automatically identify anatomical feature points from pre-operative 3D images, a task made difficult by the pelvis's complex and variable structure. The method aims to improve the accuracy of 3D/3D feature-based registration, a key step for successful surgical outcomes. While this paper has garnered 2 citations to date, its recent publication suggests growing interest in Cheng's innovative approach to automating surgical planning. By tackling the bottleneck of manual feature extraction, Cheng's research holds promise for enhancing the precision and efficiency of robot-assisted fracture reduction, potentially reducing surgical time and improving patient recovery.
Research Focus
Key Achievements
Top Papers
- 1