Associate Professor

Michel Audette

1321 Engr & Comp Sci Bldg
Norfolk, 23529

B.Eng. Electrical Engineering - McGill University (Montreal, Canada) 1982-1986
Flight simulation engineer - CAE Electronics (Montreal) 1986-1988
M.Eng. Electrical Engineering (computer vision) - Ecole Polytechnique (Montreal) - 1989-1992
Welding automation engineer (real-time computer vision) - MVSI (Montreal)
Ph.D. Biomedical Engineering (half-time) at McGill and neuronavigation engineer (half-time) at ISG Technologies (Mississauga, Canada) 1993-95
Ph.D. Biomedical Engineering (full-time) 1996-2000
Post-doc AIST Tsukuba, Japan 2001-2005
Dissertation defense McGill June 2002, Degree awarded Oct 2002
Post-doc Innovation Center Computer Assisted Surgery – ICCAS, Leipzig, Germany. 2006-Oct 2008
Software engineer, Kitware Inc, Chapel Hill NC, Oct 2008-June 2011
Assistant Professor, MSVE/CMSE, Old Dominion University, July 2011-2017
Associate Professor, MSVE/CMSE, ODU, 2017-now
GPD Biomedical Engineering, ODU, May 2020-now.

Ph.D. in Biomedical Engineering, McGill University, (2002)

Contracts, Grants and Sponsored Research

Audette, M., Shen, Y. and Morrison, S. "Sensor and simulation-based fall injury mitigation for senior citizens" $150000.00 . Other. June 1, 2017 - May 31, 2020
Audette, M., Bawab, S. and Ringleb, S. I. "Scoliosis Surgery Planning Through Cadaveric Ligamento-Skeletal Tissue Mapping And Loading Studies, Multi-Surface Segmentation, And Finite Element Simulation of The Spine" $37277.68 . Other. August 1, 2016 - July 31, 2017
Audette, M. and McKenzie, F. D. "CWIG Meeting Technology Enhancements And Preparations" $30770.00 . Commercial. February 1, 2016 - January 31, 2017

Research Interests

Main areas of research I have three main areas of research, and a number or recent interests, as follows.
Medical/Surgical simulation. This entails research on both anatomical and therapy models, featuring innovations in medical image segmentation, surface and volume meshing, and ontological modeling.
Emphasis on neurological and orthopedic surgery. Objectives are a broadly usable interactive neurosurgery simulation for resident training and predictive orthopedic surgery simulation.
Anatomical modeling involves minimally supervised Segmentation and Meshing, where segmentation emphasizes digital atlases and deformable models, both surface and contour-based.
Meshing spans all types of discretization, which varies according to anatomy: volumetric structures benefit from minimally supervised, controlled-resolution variational tetrahedral meshing, while curviplanar structures exploit discrete surface models and curvilinear structures are identified/discretized by discrete 3D contour models.
Therapy and functional models address both predictive and interactive simulation, e.g.: haptics-driven meshless cutting and functional physiology models (pilot-centered flight simulation).
Musculoskeletal simulation based on OpenSim and personalized anatomical models. Application to geriatric subject modeling (detection of impending fall). Ontological modeling centers on a high-level workflow representation of surgical procedures, particularly neurosurgery, organized according to canonical approaches: pterional, transnasal, etc. These ontological models have implications in terms what to simulate and represent in detail for surgical planning and navigation.
Also a nascent interest on bimanual glove haptics-driven obstetrics simulation.
Surgery planning. Any anatomical modeling technique developed for simulation can also be applied to surgery planning for image-guided surgery (IGS). We strive for segmentation methods more descriptive than what is available currently, especially in commercial IGS products. This is particularly true of skull base anatomy: cranial nerves, brainstem, etc.
Medical device (e.g. surgical robotics) facilitation: use of simulation and planning for medical devices. Geriatric airbag development for fall injury mitigation: both garment and smart room-based. Collaboration with EVMS Geriatrics and Gerontology (H. Okhravi MD), San Diego State University (C. Paolini PhD and M. Sarkar PhD) and Virginia Commonwealth University School of the Arts (A. Ilnicki MFA). MRI and ultrasound-guided navigation for surgical robotic assistant for minimally invasive breast tumor resection. Collaboration with surgeon Eric Feliberti MD FACS of Eastern Virginia Medical School Dept. of Surgery and Krishna Kaipa PhD of ODU Mechanical and Aerospace Engineering.

Articles

Bui, H. Phuoc., Tomar, S., Courtecuisse, H., Audette, M., Cotin, S. and Bordas, S. P.A.. (2018). Controlling the error on target motion through real‐time mesh adaptation: Applications to deep brain stimulation. International Journal for Numerical Methods in Biomedical Engineering , pp. e2958.
Audette, M., Jovanovic, V., Bilgen, O., Arcaute, K. and Dean, A. W. (2017). Creating the fleet maker: 3D printing for the empowerment of sailors. Naval Engineers Journal 129 (2) , pp. 61-68.
Rashid, T., Sultana, S., Fischer, G. S. and Audette, M. (2017). Deformable multi-material 2-simplex surface mesh for intraoperative MRI-ready surgery planning and simulation, with deep-brain stimulation applications. Lecture Notes in Computer Science.
Brix, K. A., Brody, D. L., Grimes, J. B., Yitzhak, A., Agoston, D., Aldag, M. and Audette, M. (2017). Military blast exposure and chronic neurodegeneration: Summary of working groups and expert panel findings and recommendations. Journal of Neurotrauma 34 (S1) , pp. S18-S25.
Sultana, S., Blatt, J. E.., Gilles, B., Rashid, T. and Audette, M. (2017). MRI-Based Medial Axis Extraction and Boundary Segmentation of Cranial Nerves Through Discrete Deformable 3D Contour and Surface Models. IEEE Transactions on Medical Imaging 36 (8) , pp. 1711-1721.
Zhang, J. Z., Zhang, X. X. and Audette, M. (2011). A photothermal model of selective photothermolysis with dynamically changing vaporization temperature. Lasers in Medical Science 26 (5) , pp. 633-640.
Descoteaux, M., Audette, M., Chinzei, K. and Siddiqi, K. (2008). Bone enhancement filtering: application to sinus bone segmentation and simulation of pituitary surgery. Journal of Computer Aided Surgery 11 (5) , pp. 247-255.
Audette, M., Delingette, H., Fuchs, A., Astley, O. and Chinzei, K. (2007). A Topologically Faithful, Tissue-guided, Spatially Varying Meshing Strategy for Computing Patient-specific Head Models for Endoscopic Pituitary Surgery Simulation . Journal of Computer Aided Surgery 12 (1) , pp. 43-52.
Audette, M., Siddiqi, K., Ferrie, F. P. and Peters, T. M. (2003). An Integrated Range-sensing, Segmentation and Registration Framework for the Characterization of Intra-surgical Brain Deformations in Image-Guided Surgery . Computer Vision and Image Understanding, Special issue on Nonrigid Registration, 89 (2/3) , pp. 226-251.
Audette, M., Ferrie, F. P. and Peters, T. M. (2000). An Algorithmic Overview of Surface Registration Techniques for Medical Imaging. Medical Image Analysis 4 (3) , pp. 201-217.

Book Chapters

Audette, M., Chernikov, A. and Chrisochoides, N. (2012). A Review of Mesh Generation for Medical Simulators Handbook of Real World Applications in Modeling and Simulation Wiley.
Audette, M., Miga, M. I., Nemes, J., Chinzei, K. and Peters, T. M. (2008). A Review Of Biomechanical Modeling of the Brain for Intrasurgical Displacement Estimation and Medical Simulation Biomechanical Systems, World Scientific Publishing.
Audette, M., Hertel, I., Burgert, O. and Strauss, G. (2008). A Tissue Relevance and Meshing Method for Computing Patient-specific Anatomical Models in Endoscopic Sinus Surgery Simulation Advances in Computational Vision and Medical Image Processing: Methods and Applications, Springer.
Audette, M., Siddiqi, K., Ferrie, F. P. and Peters, T. M. (2005). Brain Shift Estimation for Image Guided Surgery Based on an Integrated Range sensing, Segmentation, and Registration Framework, Medical Imaging Systems: Technology & Applications World Scientific Publishing.

Conference Proceeding

Jovanovic, V., Bilgen, O., Arcaute, K., Audette, M. and Dean, A. W. (2017). Active duty training for support of navy's additive manufacturing strategy ASEE Annual Conference and Exposition.
Audette, M., Rashid, T., Ghosh, S., Patel, N. and Sultana, S. (2017). Towards an anatomical modeling pipeline for simulation and accurate navigation for brain and spine surgery Simulation Series.
Audette, M., Riviere, D., Ewend, M. and Valette, S. (2011). Approach-guided Controlled Resolution Brain Meshing for FE-based Interactive Neurosurgery Simulation MICCAI Workshop on Mesh Processing in Medical Image Analysis.
Audette, M., Riviere, D., Law, C., Ibanez, L., Aylward, S., Finet, J., Wu, X. and Ewend, M. (2011). Approach-specific multi-grid anatomical modeling for neurosurgery simulation with public-domain and open-source software Proc Soc Photo Opt Instrum Eng.
Audette, M., Yang, H., Enquobahrie, A., Barre, S. and Ewend, M. (2011). The Application of Textbook-Based Surgical Ontologies to Neurosurgery Simulation Requirements Computer-Assisted Radiology and Surgery.
Audette, M., Kolahi, A., Enquobahrie, A., Gatti, C. and Cleary, K. R. (2010). Reducing depth uncertainty in large surgical workspaces, with applications to veterinary medicine, (pp. 762525-762525-7) SPIE Conference on Medical Imaging.
Audette, M., Gruhser, C., Ritter, N., Maass, M., Dientz, A. and Strauss, G. (2008). Achieving Interactive Biomechanical, Patient-specific Simulation of the Blakesley Forceps in Sinus Surgery Computer Aided Surgery around the Head, CAS-H.
Li, F., Strauss, G., Trantakis, C. and Audette, M. (2008). An Iterative Classification Method Of 2D CT Head Data Based On Statistical And Spatial Information Computer Aided Surgery around the Head, CAS-H.
Li, F., Bartz, D., Gu, L. and Audette, M. (2008). An Iterative Classification Method of 2D CT Head Data Based on Statistical and Spatial Information, International Conference Pattern Recognition.
Gruhser, C., Ritter, N., Strauss, G., Maass, H. and Audette, M. (2008). Development of a Tool-centered Collision Model for Volumetric Resection in ENT Surgery Simulation, EuroHaptics.
Gress, O., Lindner, D., Trantakis, C., Meizensberger, J., Strauss, G., Han, J., Hornegger, J. and Audette, M. (2008). Minimally Supervised Method for Identifying Cranial Nerves in Histological Data Computer Aided Surgery around the Head, CAS-H.
Audette, M., Brooks, R., Funnell, R., Strauss, G. and Arbel, T. (2008). Piecewise-affine initialized spline-based patient-specific registration of a high-resolution ear model for surgical guidance MICCAI Workshop on Image Guidance and Computer Assistance for Soft-Tissue Intervention.
Audette, M., Hertel, I., Burgert, O. and Strauss, G. (2007). A Tissue Relevance and Meshing Method for Computing Patient-specific Anatomical Models in Endoscopic Sinus Surgery Simulation VIPImage- ECCOMAS Thematic Conference o Computational Vision and Medical Image Processing.
Audette, M., Slowik, V., Steinke, H. and Bohme, J. (2007). Towards Minimally Supervised Patient-specific Biomechanically Guided Pelvic Trauma Surgery CompMed.
Audette, M., Delingette, H., Fuchs, A. and Chinzei, K. (2006). A Topologically Faithful, Tissue-guided, Spatially Varying Meshing Strategy for the Computation of Patient-specific Head Medicine Meets Virtual Reality.
Audette, M., Hayward, V., Koseki, Y. and Sinclair, I. (2006). Towards a flexible 7-DOF haptic device for tool-specific surgical simulation, with applications to simulating trans-nasal pituitary surgery Computer Assisted Radiology and Surgery (CARS).
Audette, M., Descoteaux, M., Delingette, H. and Chinzei, K. (2005). A Minimally Supervised Image Processing Pipeline for Computing FE-ready Anatomical Models for Neurosurgical Computer Assisted Radiology and Surgery (CARS).
Audette, M., Delingette, H., Fuchs, A., Astley, O. and Chinzei, K. (2005). A Topologically Faithful, Tissue-guided, Spatially Varying Meshing Strategy for Computing Patient-specific Head Models for Endoscopic Pituitary Surgery Simulation, (pp. 178-188) Computer Vision for Biomedical Image Applications, International Conference on Computer Vision.
Descoteaux, M., Audette, M., Chinzei, K. and Siddiqi, K. (2005). Bone enhancement filtering: application to sinus bone segmentation and simulation of pituitary surgery (pp. 9-16) Medical Image Computing and Computer-Assisted Intervention (MICCAI).
Audette, M., Astley, O., Doyon, M., Hayward, V., McCallister, G. and Chinzei, K. (2004). A PC-based System Architecture for Real-time Finite Element-based Tool-specific Surgical Simulation Computer Assisted Radiology and Surgery (CARS).
Audette, M. and Chinzei, K. (2004). The Application of Embedded and Tubular Structure to Tissue Identification for the Computation of Patient-specific Neurosurgical Simulation Models (pp. 203-210) International Symposium on Medical Simulation.
Audette, M., Delingette, H., Fuchs, A., Koseki, Y. and Chinzei, K. (2003). A Procedure for Computing Patient-specific Anatomical Models for Finite Element-based Surgical Simulation Computer Assisted Radiology and Surgery (CARS), .
Audette, M., Fuchs, A., Astley, O., Koseki, Y. and Chinzei, K. (2003). Towards Patient-specific Anatomical Model Generation for Finite Element-based Surgical Simulation International Symposium on Surgical Simulation & Soft Tissue Modeling (IS4TM).
Kataoka, H., Washio, T., Audette, M. and Mizuhara, K. (2001). A Model for Relations Between Needle Deflection, Force and Thickness on Needle Penetration (pp. 996-974) Medical Image Computing and Computer-Assisted Intervention (MICCAI).

Presentations

Audette, M. (January , 2006). Panel chair, Engineering Technologies in Simulation International Conference on Medical Simulation San Diego California.
Audette, M. (January , 2005). Towards Patient-Specific Finite Element-based Surgical Simulation: Research Issues Relating to Visual, Constitutive and Clinical Realism and A Review of Public Domain Software for Computing Patient-specific Anatomical Models International Conference on Medical Simulation Miami.