Computer-Aided Surgery, Computer Vision, Medical Augmented Reality
Profession
Senior Software Developer / Master Student
Projects
Master Thesis: Radiation-Free Drill Guidance for the Interlocking of Intramedullary Nails using a Video-Augmented-Fluoroscopic Device.
The interlocking of intramedullary nails is a technically demanding procedure
which involves an excessive amount of X-ray acquisitions; one study lists as
many as 48 to successfully complete the procedure and fix screws into 5 mm holes
in the nail. In this work, a novel low cost radiation-free drilling guide is
designed to assist surgeons in completing the distal locking procedure without
any X-ray acquisitions. Using an augmented reality fluoroscope that
coregisters optical and X-ray images, we exploit solely the optical images
to detect the drilling guide in order to estimate the tip position in the
real-time in X-ray. To evaluate the accuracy of the tip estimation,
we tested over 200 random drill guide poses showing a mean tip-estimation
error of 1.72 +/- 0.7 mm which is significantly robust for the interlocking.
Also, three expert surgeons participated in a preclinical study on dry bone
phantom and successfully completed the distal locking 56 out of 60 trials with
no X-ray acquisition for guidance and an average time of 2 minutes.
These promising results have shown the robustness and accuracy of our
method thereby offering a low cost and efficient solution in decreasing the
complexity of interlocking of intramedullary nails procedure.
Supervision: Pascal Fallavollita, Lejing Wang
IDP: Markerless Motion Capture in the Operating Room
Goal of this IDP was to extend the patch-based surface tracking algorithm to a skeleton
based solution. Such a solution could be used to track surgeons in an operating room and enhance
human-machine interaction paradigms in this context. We implemented a model-based motion capture
algorithm based on 3D point cloud data. An Expectation-Maximisation algorithm computes the point
cloud/body parts assignment and the pose estimation is based on an inverse kinematic framework.
Supervision: Cédric Cagniart
Results
Report
Technical Report: IDP Project Markerless Motion Capture in the Operating Room.
HIWI: 3D Reconstruction Multi-Camera Studio
I supported documentation and follow-up works on the Real-Time 3D Reconstruction Studio of the chair,
as well as the implementation of the skeleton based motion capture component.
Master Courses
Computer Graphics and Image Understanding
IN2018 Augmented Reality
IN2017 Computer Graphics
IN2021 Computer-Aided Medical Procedures I
IN2022 Computer-Aided Medical Procedures II
IN2015 Images Synthesis
Artificial Intelligence and Robotics
IN2057 3D Computer Vision
IN2123 3D Computer Vision II
IN2124 Tracking and Detection in Computer Vision
Formal Methods and their Applications
IN2041 Automata and Formal Languages
Soft Skills
IN9007 SET Tutor
WI2004 Technology, Economy, Society
IN9003 Intellectual Property
Graduate Lab Courses
IN2106/IN8904 Computational Photography
IN2106 Medical Augmented Reality
Master Seminar
IN2107 Machine Learning Methods for Computer Vision Applications
Additional
EI7223 Information Retrieval in High Dimensional Data