Whole Image Synthesis Using a Deep Encoder-Decoder Network

Sevetlidis, Vasileios; Giuffrida, Mario Valerio; Tsaftaris, Sotirios A.

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.

• Presentation Conference Type: Conference Paper (Published)
• Conference Name: SASHIMI Workshop - MICCAI
• Start Date: Oct 17, 2016
• End Date: Oct 21, 2016
• Acceptance Date: Jul 19, 2016
• Online Publication Date: Sep 23, 2016
• Publication Date: Sep 23, 2016
• Deposit Date: Sep 24, 2019
• Publisher: Springer
• Pages: 127-137
• Series Title: Lecture Notes in Computer Science
• Series Number: 9968
• Series ISSN: 0302-9743
• Book Title: Simulation and Synthesis in Medical Imaging
• DOI: https://doi.org/10.1007/978-3-319-46630-9_13
• Public URL: http://researchrepository.napier.ac.uk/Output/2158276
• Publisher URL: https://link.springer.com/book/10.1007/978-3-319-46630-9