Whole Image Synthesis Using a Deep Encoder-Decoder Network

• Published in: Simulation and Synthesis in Medical Imaging Vol. 9968; pp. 127 - 137
• Main Authors: Sevetlidis, Vasileios, Giuffrida, Mario Valerio, Tsaftaris, Sotirios A.
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 2016; Springer International Publishing
• Series: Lecture Notes in Computer Science
• Subjects: 3D graphics & modelling; Autoencoder; Image processing; Image synthesis; MRI; Pattern recognition; Stacked neural network
• DOI: 10.1007/978-3-319-46630-9_13
• ISBN: 3319466291; 9783319466293
• ISSN: 0302-9743

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.