It’s a great pleasure to welcome Prof. Dr. Mathias Unberath back to FAU.

Abstract: Differentiably connecting 2- and 3-dimensional domains is of substantial interest in medical imaging as it enables transformational image processing techniques that substantially add value without disrupting clinical workflow. In this talk, I will introduce our recent advances in dense 3D reconstruction and differentiable rendering using examples in endoscopic and X-ray-guided surgery.

Short Bio: Mathias Unberath is an Assistant Professor in the Department of Computer Science at Johns Hopkins University with affiliations to the Laboratory for Computational Sensing and Robotics and the Malone Center for Engineering in Healthcare. He has created and is leading the Advanced Robotics and Computationally AugmenteD Environments (ARCADE) Lab that conducts research at the intersection of computer vision, machine learning, augmented reality, robotics, and medical imaging to develop collaborative systems that assist clinical professionals across the healthcare spectrum.

Previously, Mathias was an Assistant Research Professor in Computer Science and postdoctoral fellow in the Laboratory for Computational Sensing and Robotics at Hopkins and completed his Ph.D. in Computer Science at the Friedrich-Alexander-Universität Erlangen-Nürnberg from which he graduated summa cum laude in 2017. While completing a Bachelor’s in Physics and Master’s in Optical Technologies at FAU Erlangen, Mathias studied at the University of Eastern Finland as an ERASMUS scholar in 2011 and joined Stanford University as a DAAD fellow throughout 2014.

Mathias has published more than 80 journal and conference articles and has received numerous awards, grants, and fellowships, including the NIH NIBIB R21 Trailblazer Award.

References

• Gao, C., Liu, X., Gu, W., Killeen, B., Armand, M., Taylor, R., & Unberath, M. (2020). Generalizing Spatial Transformers to Projective Geometry with Applications to 2D/3D Registration. MICCAI 2020.
• Gu, W., Gao, C., Grupp, R., Fotouhi, J., & Unberath, M. (2020, October). Extended Capture Range of Rigid 2D/3D Registration by Estimating Riemannian Pose Gradients. In International Workshop on Machine Learning in Medical Imaging (pp. 281-291). Springer, Cham.
• Grupp, R. B., Unberath, M., Gao, C., Hegeman, R. A., Murphy, R. J., Alexander, C. P., ... & Taylor, R. H. (2020). Automatic annotation of hip anatomy in fluoroscopy for robust and efficient 2D/3D registration. International Journal of Computer Assisted Radiology and Surgery, 1-11.
• Liu, X., Sinha, A., Ishii, M., Hager, G. D., Reiter, A., Taylor, R. H., & Unberath, M. (2019). Dense depth estimation in monocular endoscopy with self-supervised learning methods. IEEE transactions on medical imaging, 39(5), 1438-1447.
• Liu, X., Zheng, Y., Killeen, B., Ishii, M., Hager, G. D., Taylor, R. H., & Unberath, M. (2020). Extremely Dense Point Correspondences using a Learned Feature Descriptor. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 4847-4856).
• Liu, X., Stiber, M., Huang, J., Ishii, M., Hager, G. D., Taylor, R. H., & Unberath, M. (2020). Reconstructing Sinus Anatomy from Endoscopic Video--Towards a Radiation-free Approach for Quantitative Longitudinal Assessment. MICCAI 2020.

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Music Reference: 
Damiano Baldoni - Thinking of You (Intro)
Damiano Baldoni - Poenia (Outro)