Deep Learning for Ultrasound Analysis

For several decades, using ultrasound imaging in a clinical setting meant choosing some application-specific parameters, acquiring a set of images manually, and analyzing them based on prior anatomical knowledge and imaging experience. Today, the AI revolution in the medical field also encompasses ultrasound, adding great benefit through learning-based analysis, e.g. anatomy detection or classification. But the versatility and the real-time nature of ultrasound allows us to go even further, fundamentally changing how images are acquired. In this webinar, we will discuss novel approaches to image acquisition, processing, and visualization that have the potential to radically change clinical practice and transform the ultrasound probe into an ever-more-indispensable point-of-care tool. After an introduction of NVIDIA's CLARA platform, we will look beyond conventional image processing and show how more original applications of recent GPU-Based technologies like deep learning enable real breakthroughs in both diagnostics and interventions.

By watching this webinar replay you'll learn:
  1. about NVIDIA CLARA and how it enables the medical industry to build and deploy breakthrough algorithms
  2. how to automate calibration algorithms and camera-like "auto-focus" based on real-time target detection
  3. how to revisit the full imaging pipeline with innovative real-time solutions including image enhancement, anatomy classification, or even 3D reconstruction of 2D clips

Presented By
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Dr. Oliver Zettinig
Research Scientist, ImFusion GmbH

Oliver Zettinig holds a Master's degree in Biomedical Computing and pursued his PhD on advanced, interventional ultrasound imaging techniques at Technische Universität München. During his academic career, he conducted research visits at Siemens Corporate Technology in Princeton, NJ, and Johns Hopkins University in Baltimore, MD, and received the MICCAI Young Scientist Award in 2013. Oliver joined ImFusion in February 2017 and strives to further push the boundaries in ultrasound-guided navigated procedures.
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Dr. Raphael Prevost
Senior Research Scientist, ImFusion GmbH

Raphael Prevost studied in France where he obtained a MSc. in applied mathematics and a MSc. in machine learning and computer vision in 2010. He then spent three years in Philips Research working on image segmentation during his PhD. He authored multiple articles, book chapters and patents; he is laureate of the Dauphine Foundation 2014 Award, received the AMIES 2014 Prize for research in mathematics with industrial impact, and the MICCAI 2015 best reviewer award. Since 2013, Raphael has worked on various image analysis topics and has driven R&D in terms of image segmentation and machine learning at ImFusion.
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Nicola Rieke
Senior Solutions Architect for Deep Learning in Healthcare, NVIDIA

Nicola Rieke is a solution architect at NVIDIA for deep learning in healthcare with several years of experience in the intersection of mathematics, medicine and computer science. With a broad expertise in the field of medical image processing, computer-aided medical procedures, and applied machine learning, her primary responsibility is to support the medical imaging community in advancing deep learning solutions. She published various peer reviewed papers, in particular on real-time machine learning approaches for computer assistance in surgical interventions and was honored with the prestigious MICCAI Young Scientist Award during her doctoral study at the Technical University of Munich.
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