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Whole Image Synthesis Using a Deep Encoder-Decoder Network

Sevetlidis, Vasileios and Giuffrida, Mario Valerio and Tsaftaris, Sotirios A. Whole Image Synthesis Using a Deep Encoder-Decoder Network. In: Simulation and Synthesis in Medical Imaging. First International Workshop, SASHIMI 2016, Held in Conjunction with MICCAI 2016, Athens, Greece, October 21, 2016, Proceedings. Lecture Notes in Computer Science (9968). Springer International Publishing, pp. 127-137. ISBN 978-3-319-46629-3 (2016)

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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.

Item Type: Book Section
Identification Number: 10.1007/978-3-319-46630-9_13
Subjects: Q Science > QA Mathematics > QA76 Computer software
T Technology > T Technology (General)
Research Area: Computer Science and Applications
Depositing User: Caterina Tangheroni
Date Deposited: 14 Nov 2016 11:24
Last Modified: 14 Nov 2016 11:24
URI: http://eprints.imtlucca.it/id/eprint/3595

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