TY  - CHAP
T3  - Lecture Notes in Computer Science
TI  - Whole Image Synthesis Using a Deep Encoder-Decoder Network
SN  - 978-3-319-46629-3
T2  - Simulation and Synthesis in Medical Imaging. First International Workshop, SASHIMI 2016, Held in Conjunction with MICCAI 2016, Athens, Greece, October 21, 2016, Proceedings
AV  - none
Y1  - 2016///
PB  - Springer International Publishing
EP  - 137
A1  - Sevetlidis, Vasileios
A1  - Giuffrida, Mario Valerio
A1  - Tsaftaris, Sotirios A.
ID  - eprints3595
N2  - 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.
UR  - http://dx.doi.org/10.1007/978-3-319-46630-9_13
SP  - 127
ER  -