Research Projects
3D Quantitative Analysis & Surgical Planning
We have developed an AI-driven pipeline for the quantitative analysis of the spine. We have established an unsupervised deep learning framework called MRI-SegFlow for the segmentation of multiple spinal tissues in spinal MRI, which achieved comparable performance with the state-of-art supervised methods and without relying on any manually masked ground truth.
3D Quantitative Analysis & Surgical Planning
We have developed an AI-driven pipeline for the quantitative analysis of the spine. We have established an unsupervised deep learning framework called MRI-SegFlow for the segmentation of multiple spinal tissues in spinal MRI, which achieved comparable performance with the state-of-art supervised methods and without relying on any manually masked ground truth.
Multi-View Mark-Less Surgical Navigation System
We have developed an AI-driven pipeline for the quantitative analysis of the spine. We have established an unsupervised deep learning framework called MRI-SegFlow for the segmentation of multiple spinal tissues in spinal MRI, which achieved comparable performance with the state-of-art supervised methods and without relying on any manually masked ground truth.
Multi-View Mark-Less Surgical Navigation System
We have developed an AI-driven pipeline for the quantitative analysis of the spine. We have established an unsupervised deep learning framework called MRI-SegFlow for the segmentation of multiple spinal tissues in spinal MRI, which achieved comparable performance with the state-of-art supervised methods and without relying on any manually masked ground truth.
Medical Image Registration and Navigation
In this project, we aim to investigate and develop novel image registration techniques for medical imaging analysis. We explore different registration methods, including intensity-based, feature-based, and learning-based, and evaluate their performance on various medical imaging modalities, such as CT, MR, optical imagery etc. The outcomes of this project will have significant implications for the clinical use of medical imaging technologies. The developed image registration algorithms can be used to improve the accuracy and efficiency of clinical decision-making and treatment planning, leading to better patient outcomes.
Medical Image Registration and Navigation
In this project, we aim to investigate and develop novel image registration techniques for medical imaging analysis. We explore different registration methods, including intensity-based, feature-based, and learning-based, and evaluate their performance on various medical imaging modalities, such as CT, MR, optical imagery etc. The outcomes of this project will have significant implications for the clinical use of medical imaging technologies. The developed image registration algorithms can be used to improve the accuracy and efficiency of clinical decision-making and treatment planning, leading to better patient outcomes.
Orthopaedic Biomechanics and Implant Design
We have demonstrated that trabecular microarchitecture (TraMicroArcht) can vary the apparent modulus (E), yield strength of trabecular bone accurately. Micro-finite element analysis can quantify the effects of TraMicroArcht on mechanical properties. MicroCT images can be converted directly into digital models via our in-house developed new system and simulate virtual loading effects. Our new system can also have significant application in fast implant design and optimization.
Orthopaedic Biomechanics and Implant Design
We have demonstrated that trabecular microarchitecture (TraMicroArcht) can vary the apparent modulus (E), yield strength of trabecular bone accurately. Micro-finite element analysis can quantify the effects of TraMicroArcht on mechanical properties. MicroCT images can be converted directly into digital models via our in-house developed new system and simulate virtual loading effects. Our new system can also have significant application in fast implant design and optimization.
Machine-learning Modeling & Magnetic Monitoring for Scoliosis Correction (M4Sc)
M4Sc uses AI technology and intelligent theater to reduce risks and complications while improving treatment success and patient survival. The project benefits include increasing surgical planning efficiency, providing real-time risk warnings, and reducing radiation exposure and patient discomfort. It also offers accessible and frequent tracking of corrections and low-cost equipment without compromising accuracy. The project aims to improve patients’quality of life and reduce the impact of scoliosis on their health.
Machine-learning Modeling & Magnetic Monitoring for Scoliosis Correction (M4Sc)
M4Sc uses AI technology and intelligent theater to reduce risks and complications while improving treatment success and patient survival. The project benefits include increasing surgical planning efficiency, providing real-time risk warnings, and reducing radiation exposure and patient discomfort. It also offers accessible and frequent tracking of corrections and low-cost equipment without compromising accuracy. The project aims to improve patients’quality of life and reduce the impact of scoliosis on their health.