Robust Multi-Modal MR Image Synthesis

Thomas Joyce*, Agisilaos Chartsias, Sotirios Tsaftaris

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We present a multi-input encoder-decoder neural network model able to perform MR image synthesis from any subset of its inputs, outperforming prior methods in both single and multi-input settings. This is achieved by encouraging the network to learn a modality invariant latent embedding during training. We demonstrate that a spatial transformer module [7] can be included in our model to automatically correct misalignment in the input data. Thus, our model is robust both to missing and misaligned data at test time. Finally, we show that the model’s modular nature allows transfer learning to different datasets.

Original languageEnglish
Title of host publicationMedical Image Computing and Computer-Assisted Intervention-MICCAI 2017
Subtitle of host publication20th International Conference, Quebec City, QC, Canada, September 11-13, 2017, Proceedings, Part III
PublisherSpringer-Verlag
Pages347-355
Number of pages9
ISBN (Electronic)978-3-319-66179-7
ISBN (Print)978-3-319-66178-0
DOIs
Publication statusPublished - 4 Sep 2017
Event20th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2017 - Quebec City, Canada
Duration: 11 Sep 201713 Sep 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10435 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference20th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2017
CountryCanada
CityQuebec City
Period11/09/1713/09/17

Keywords

  • Brain
  • MRI
  • Neural network
  • Synthesis

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