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2016 | OriginalPaper | Buchkapitel

Whole Image Synthesis Using a Deep Encoder-Decoder Network

verfasst von : Vasileios Sevetlidis, Mario Valerio Giuffrida, Sotirios A. Tsaftaris

Erschienen in: Simulation and Synthesis in Medical Imaging

Verlag: Springer International Publishing

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Abstract

The synthesis of medical images is an intensity transformation of a given modality in a way that represents an acquisition with a different modality (in the context of MRI this represents the synthesis of images originating from different MR sequences). Most methods follow a patch-based approach, which is computationally inefficient during synthesis and requires some sort of ‘fusion’ to synthesize a whole image from patch-level results. In this paper, we present a whole image synthesis approach that relies on deep neural networks. Our architecture resembles those of encoder-decoder networks, which aims to synthesize a source MRI modality to an other target MRI modality. The proposed method is computationally fast, it doesn’t require extensive amounts of memory, and produces comparable results to recent patch-based approaches.

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Titel: Whole Image Synthesis Using a Deep Encoder-Decoder Network
verfasst von: Vasileios Sevetlidis
Mario Valerio Giuffrida
Sotirios A. Tsaftaris
Verlag: Springer International Publishing
Buch: Simulation and Synthesis in Medical Imaging
Print ISBN: 978-3-319-46629-3

Electronic ISBN: 978-3-319-46630-9

Copyright-Jahr: 2016
DOI: https://doi.org/10.1007/978-3-319-46630-9_13

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