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.
CITATION STYLE
Sevetlidis, V., Giuffrida, M. V., & Tsaftaris, S. A. (2016). Whole image synthesis using a deep encoder-decoder network. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9968 LNCS, pp. 127–137). Springer Verlag. https://doi.org/10.1007/978-3-319-46630-9_13
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