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A machine learning pipeline for internal anatomical landmark embedding based on a patient surface model.

08:00 EDT 13th October 2018 | BioPortfolio

Summary of "A machine learning pipeline for internal anatomical landmark embedding based on a patient surface model."

With the recent introduction of fully assisting scanner technologies by Siemens Healthineers (Erlangen, Germany), a patient surface model was introduced to the diagnostic imaging device market. Such a patient representation can be used to automate and accelerate the clinical imaging workflow, manage patient dose, and provide navigation assistance for computed tomography diagnostic imaging. In addition to diagnostic imaging, a patient surface model has also tremendous potential to simplify interventional imaging. For example, if the anatomy of a patient was known, a robotic angiography system could be automatically positioned such that the organ of interest is positioned in the system's iso-center offering a good and flexible view on the underlying patient anatomy quickly and without any additional X-ray dose.

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This article was published in the following journal.

Name: International journal of computer assisted radiology and surgery
ISSN: 1861-6429
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