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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, Nassir Navab
![]() Watch the talk given at MICCAI 2012 online ... |
A first draft of a CamC simulation based on blender's game engine. Digitally reconstructed radiographs (DRR) are produced over calls to our C++ DRR generation component over the blender python API. The user moves the C-Arm over the keyboard to a desire pose and takes radiographs. Those radiographs are overlaid over the video stream shown on screen. |
See chair's project page at CamC - Camera Augmented Mobile C-arm.
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
![]() | Technical Report: IDP Project Markerless Motion Capture in the Operating Room. |
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.
Languages | Frameworks and Libraries | Tools |
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C/C++ | OpenGL | Eclipse CDT/JDT |
Python | OpenCV | CMake/Ant |
CUDA | Qt/QGLviewer | Blender |
LaTeX2e | JavaCC/Lex/Yacc | GIT/Subversion |
Matlab | Tomcat/Spring/ZK | Visual Studio |
JAVA | OpenNI | JIRA/Jenkins |
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Title: | Master |
Firstname: | Benoit |
Middlename: | |
Lastname: | Diotte |
Picture: | ![]() |
Birthday: | |
Nationality: | Canada |
Languages: | English, German, French |
Groups: | Surgical Workflow, Computer-Aided Surgery, Computer Vision, Medical Augmented Reality |
Expertise: | Surgical Workflow, Computer-Aided Surgery, Computer Vision, Medical Augmented Reality |
Position: | External Collaborator |
Status: | Active |
Emailbefore: | bdiott |
Emailafter: | gmx.de |
Room: | NARVIS Lab |
Telephone: | +49 176 21302082 |
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Defensedate: | 1 June 2012 |
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