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AutoSyncoder: An Adversarial AutoEncoder Framework for Multimodal MRI Synthesis
, Raju J.C., Keerthi Ram, Murugesan B.
Published in Springer Nature
Volume: 12450 LNCS
Pages: 102 - 110
The ability to generate multiple contrasts for the same patient is unique about MRI and of very high clinical value. In this work, we take up the problem of modality synthesis in multimodal MRI and propose an efficient, multiresolution encoder-decoder network trained like an autoencoder that can predict missed inputs at the output. This can help in avoiding the acquisition of redundant information, thereby saving time. We formulate and demonstrate our proposed AutoSyncoder network in a GAN and cyclic GAN setting, and evaluate on the BRATS-15 multimodal glioma dataset. A PSNR ranging between 29 to 30.5 dB, and SSIM over 0.88 is achieved for all the modalities, with simplistic training, thereby establishing the potential of our approach. © 2020, Springer Nature Switzerland AG.
About the journal
JournalData powered by TypesetLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherData powered by TypesetSpringer Nature
Open AccessNo