BenoitDiotte

Chair for Computer Aided Medical Procedures & Augmented Reality
Lehrstuhl für Informatikanwendungen in der Medizin & Augmented Reality

Benoit Diotte
Email Benoit.Diotte@cib.de
Room NARVIS Lab
Phone +49 176 21302082
Groups 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

Results

Thesis(soon available)

See chair's project page at CamC - Camera Augmented Mobile C-arm.

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
    • IN2016 Image Understanding
    • IN2126 Software Engineering I
    • IN2012 Parallel Numerics
    • IN2088 Software Architectures

Preferred Tools and Languages

Languages Frameworks and Libraries Tools
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   JIRA/Jenkins

Curriculum Vitae

CV English



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Title:  
Firstname: Benoit
Middlename:  
Lastname: Diotte
Picture: portrait_casual.jpg
Birthday:  
Nationality: Canada
Languages: English, German, French
Groups: Computer-Aided Surgery, Computer Vision, Medical Augmented Reality
Expertise: Computer Vision
Position: Master Thesis
Status: Active
Emailbefore: Benoit.Diotte
Emailafter: cib.de
Room: NARVIS Lab
Telephone: +49 176 21302082
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Edit | Attach | Refresh | Diffs | More | Revision r1.39 - 19 May 2012 - 14:12 - BenoitDiotte

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