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Title
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2D/3D registration with a statistical deformation model prior using deep learning
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Author
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Abstract
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| Deep learning-based (DL) solutions are increasingly been adopted for 2D/3D registration as they can achieve faster 3D reconstructions from 2D radiographs compared to classical methods. This study proposes a novel semi-supervised DL-network for 2D/3D registration, in which an atlas is registered to two orthogonal radiographs. The deformation of the atlas is composed of an affine transformation and a local deformation constrained by a B-spline-based statistical deformation model. The network has been validated on digitally reconstructed radiographs of femur CT images. |
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Language
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English
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Source (book)
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2021 IEEE EMBS International Conference on Biomedical and Health Informatics (BHI), 27-30 July 2021, Athens, Greece
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Publication
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2021
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ISBN
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978-1-6654-0358-0
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DOI
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10.1109/BHI50953.2021.9508540
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Volume/pages
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(2021)
, p. 1-4
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Full text (Publisher's DOI)
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Full text (open access)
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Full text (publisher's version - intranet only)
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