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Medical Imaging A Roadmap Takes Shape


With this vision of tomorrow's integrated treatment environment in mind, researchers at Siemens Corporate Technology have developed learning-based software that can identify and segment any organ in digital medical images, regardless of occlusions, angle of view, imaging modality, or pathology.


An example of this is a heart model segmentation software that automatically separates the heart from a 3-D CT or MR image set. When used in combination with live fluoroscopy, segmented heart models can be used to locate the exact areas on the heart's surface to be ablated in order to neutralize arrhythmia-causing tissues.


In addition, at the U.S. National Institutes of Health (NIH) in Bethesda, Maryland, live image-model fusion software, developed by Siemens Corporate


Technology in


cooperation with Siemens Healthcare, has been used experimentally to help guide an artificial valve to its target in a pig's heart. “This fusion of heart models and live images provides the landmarks that help physicians identify exactly where a catheter is located in real-time,” says Yu.


Working along similar lines, Razvan Ionasec, PhD, a specialist in machine learning applications for medical imaging at Siemens Imaging & Therapy Systems Division in Forcheim, Germany, is combining pre-operative 3-D CT


images with 2-D X-ray video images generated in the operating room by a Siemens “C-arm” CT scanner. “What typically happens,” he explains “is that before an operation, you have a lot of high-resolution equipment and time to produce images. But what you want is to make the high-resolution images and information that is taken pre-op available in the operating room, where time is short and imaging power is limited,” he says “To bridge this gap, the pre-op information is mapped to the fluoroscopy data. As a result, you have real-time motion information — something


you would fluoroscopy alone.”


“This fusion of heart models and live images provides the landmarks that help physicians identify exactly where a catheter is located in real-time.”


Daphne Yu, Head of Visualization Lab at Siemens Corporate Technology in Princeton, New Jersey.


never be able to get from


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INSIGHT ON


HOSPITAL & HEALTHCARE MANAGEMENT VOL. 3 ISSUE 3 August 2014


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