Philips Research Hamburg Aerial

Philips Research is a global organization that helps Philips introduce meaningful innovations that improve people’s lives. We provide technology options for innovations in the area of health and wellbeing, targeted at both developed and emerging markets. Positioned at the front-end of the innovation process, we work on everything from spotting trends and ideation to proof of concept and – where needed – first-of-a-kind product development. In order to fulfill our ambition, it is crucial that we work together with companies that are complementary to Philips and share our vision. Philips Research, as one of the pioneers of open innovation, is actively leveraging its deep competences, know-how and IP to work with selected companies and organizations with the purpose of creating win-win propositions. This entails working with potential strategic partners, future suppliers of our businesses, and with companies that provide us with a broader window on the world.

Philips Technologie GmbH Innovative Technologies (PTIT) is the German part of Philips Research concentrating mainly on medical imaging and medical image analysis. Philips Technologie GmbH Innovative Technologies has a long history in many aspects of medical imaging, in particular in hardware and methods for Magnetic Resonance imaging as well as image processing, e.g. segmentation for radiation therapy planning. PTIT actively participated in several projects in the context of the Virtual Physiological Human. The latest relevant research EU-funded projects were ContraCancrum and EUHeart.

URL

http://www.research.philips.com/locations/hamburg.html

Key publications

J. Peters et al., ” Automatic Whole Heart Segmentation in Static Magnetic Resonance Image Volumes”, Proc. MICCAI 2007, Lecture Notes in Computer Science Volume 4792, 2007, pp 402-410, 2007.

T. Vik et al, ” A new method for robust organ positioning in CT images”, Proc. ISBI 2012, p. 338, 2012.

S. Remmele et al., ” Concurrent MR blood volume and vessel size estimation in tumors by robust and simultaneous ΔR2 and ΔR2* quantification”, Magn. Res. Med. 66(1), 144, 2011.

A. Franz, S. Remmele and J. Keupp, “Brain tumour segmentation and tumour tissue classification based on multiple MR protocols”, Proc. SPIE 7962, Medical Imaging 2011: Image Processing, 79622O (March 14, 2011).

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