Zeit: 29.11.2019, 15:00
Ort: Raum 072 im Fraunhofer IGD, Fraunhoferstrasse 5, S3|05
Referent: Yannick Pflanzer (Betreuer: Martin Ritz)
Titel: „Phenomenological Acquisition and Rendering of Optical Material Behavior for Entire 3D Objects“ (Bachelorbeit)
Abstract: In the last few years, major improvements in 3D scanning and rendering technology have been accomplished. Especially the acquisition of surface appearance information has seen innovation thanks to phenomenological approaches for capturing lighting behavior. In this work, the current Bi-directional Texturing Function (BTF) and Approximate-BTF (ABTF) approaches were extended to allow for a greater depth of effects to be captured as well as the ability to reproduce entire 3D objects from different viewing angles.
The proposed Spherical Harmonic BTF (SHBTF) is able to model the captured surface appearance of objects by encoding all measured light samples into spherical harmonic coefficients, allowing for calculation of the surface appearance for any given light direction.
In contrast to the ABTF, an SHBTF can capture multiple views of the same object which enables it to efficiently reproduce anisotropic material properties and subsurface scattering in addition to the spatially varying effects captured by an ABTF.
The CultArc3D capturing setup used for all measurements is versatile enough to deliver view and light samples from a full hemisphere around an arbitrary object. It is now possible to capture entire 3D objects as opposed to many other BTF acquisition techniques. Challenges for the SH based lighting solution are ringing artifacts, growing stronger with rising SH bands.
Another challenge for a full 3D experience was the re-projection of camera images onto a 3D model, depending heavily on the camera hardware calibration. The SH based approach has the potential to produce compelling results given further optimizations of the SH and re-projection accuracy.
Zeit: 04.12.2019, 13:30-14:30
Ort: Raum 324 im Fraunhofer IGD, Fraunhoferstrasse 5, S3|05
Referentin: Dr. Sandy Engelhardt
University of Applied Sciences in Mannheim
Titel: „Deep Learning-based Image Analysis and Augmented Reality for Cardiology and Cardiac Surgery“
Abstract: Cardiac imaging improves on diagnosis of cardiovascular diseases by providing images at high spatiotemporal resolution. Manual evaluation of these time-series, however, is expensive and prone to biased and non-reproducible outcomes. At the same time, convolutional neural networks have shown outstanding performance in medical image processing. We present CNN methods that address named limitations by integrating segmentation, disease classification and motion modelling into a fully automatic processing pipeline. We will reflect on our winning contribution for MICCAI's 2017 Automated Cardiac Diagnosis Challenge (ACDC@STACOM workshop). Furthermore, we will show results from the translation of these methods to real heterogeneous clinical data and we provide information on the remaining performance gap.
In the second half of the talk, we will focus on computer-assisted surgery. One of our main research aims is to increase objectivism in surgery by the help of quantifications and better training concepts. We will present novel visualizations and measurement approaches of the mitral valve to improve performance in complex valve repair surgeries. Beyond that, we present enhanced tools for surgical training and unique concepts for phantoms based on 'Hyperrealism' (GANs). Towards the end of the talk, our initiative at research campus STIMULATE (University Magdeburg) is presented, where we mainly focus on echocardiography and tracking together with partners from industry.
Bio: Sandy Engelhardt is currently Post-Doc at University of Applied Sciences in Mannheim. She is PI of a DFG funded project and leader of the research group “Heart” at the research campus STIMULATE at Magdeburg University. The focus of STIMULATE are technologies for image guided minimally invasive methods in medicine. She studied Computational Visualistics at University of Koblenz-Landau and received the M.Sc. degree in 2012. From 2012 to 2016, she did her Ph.D. at the ”German Cancer ResearchCenter” (DKFZ) in Heidelberg in the division of ”Medical and Biological Informatics”. During her Ph.D., she was part of the Collaborative Research Center SFB/TRR 125 Cognition-guided surgery and contributed to an assistance system for reconstructive mitral valve surgery. The system was awarded with the MICCAI AE-CAI Best Paper Award in 2014. Her dissertation ”Computer-assisted Quantitative Mitral Valve Surgery” granted the BVM-Award 2017 for the best PhD thesis on the German Community. At MICCAI 2017, she together with a team won the ”Automated Cardiac Diagnosis Challenge”(ACDC) at the STACOM Workshop using a Deep Learning Approach. Her work mainly addresses topics of cardiology and computer-assisted cadiac surgery, thereby combining methods and technologies from the field of image segmentation, tracking systems and augmented reality.