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A deep learning framework for automatic detection of arbitrarily shaped fiducial markers in intrafraction fluoroscopic images.

08:00 EDT 31st March 2019 | BioPortfolio

Summary of "A deep learning framework for automatic detection of arbitrarily shaped fiducial markers in intrafraction fluoroscopic images."

Real-time image guided adaptive radiation therapy (IGART) requires accurate marker segmentation to resolve 3D motion based on 2D fluoroscopic images. Most common marker segmentation methods require prior knowledge of marker properties to construct a template. If marker properties are not known, an additional learning period is required to build the template which exposes the patient to an additional imaging dose. This work investigates a deep learning-based fiducial marker classifier for use in real-time IGART that requires no prior patient-specific data or additional learning periods. The proposed tracking system uses convolutional neural network (CNN) models to segment cylindrical and arbitrarily shaped fiducial markers.

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This article was published in the following journal.

Name: Medical physics
ISSN: 2473-4209
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