Adversarial Image Synthesis for Unpaired Multi-Modal Cardiac Data

Agisilaos Chartsias*, Thomas Joyce, Rohan Dharmakumar, Sotirios A. Tsaftaris

*Corresponding author for this work

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

Abstract

This paper demonstrates the potential for synthesis of medical images in one modality (e.g. MR) from images in another (e.g. CT) using a CycleGAN [24] architecture. The synthesis can be learned from unpaired images, and applied directly to expand the quantity of available training data for a given task. We demonstrate the application of this approach in synthesising cardiac MR images from CT images, using a dataset of MR and CT images coming from different patients. Since there can be no direct evaluation of the synthetic images, as no ground truth images exist, we demonstrate their utility by leveraging our synthetic data to achieve improved results in segmentation. Specifically, we show that training on both real and synthetic data increases accuracy by 15% compared to real data. Additionally, our synthetic data is of sufficient quality to be used alone to train a segmentation neural network, that achieves 95% of the accuracy of the same model trained on real data.

Original languageEnglish
Title of host publicationSimulation and Synthesis in Medical Imaging
Subtitle of host publicationSecond International Workshop, SASHIMI 2017, Held in Conjunction with MICCAI 2017, Québec City, QC, Canada, September 10, 2017, Proceedings
EditorsSotirios Tsaftaris, Ali Gooya, Alejandro Frangi, Jerry Prince
PublisherSpringer International Publishing
Pages3-13
Number of pages11
Volume10557
Edition1
ISBN (Electronic)978-3-319-68127-6
ISBN (Print)9783319681269
DOIs
Publication statusPublished - 26 Sep 2017
Event2nd International Workshop on Simulation and Synthesis in Medical Imaging, SASHIMI 2017 Held in Conjunction with the 20th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2017 - Quebec City, Canada
Duration: 10 Sep 201710 Sep 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10557 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference2nd International Workshop on Simulation and Synthesis in Medical Imaging, SASHIMI 2017 Held in Conjunction with the 20th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2017
CountryCanada
CityQuebec City
Period10/09/1710/09/17

Keywords

  • Cardiac
  • CT
  • Deep learning
  • GAN
  • MR
  • Synthesis

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