Unpaired deep cross-modality synthesis with fast training

Lei Xiang, Yang Li, Weili Lin, Qian Wang, Dinggang Shen

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

3 Citations (Scopus)

Abstract

Cross-modality synthesis can convert the input image of one modality to the output of another modality. It is thus very valuable for both scientific research and clinical applications. Most existing cross-modality synthesis methods require large dataset of paired data for training, while it is often non-trivial to acquire perfectly aligned images of different modalities for the same subject. Even tiny misalignment (i.e., due patient/organ motion) between the cross-modality paired images may place adverse impact to training and corrupt the synthesized images. In this paper, we present a novel method for cross-modality image synthesis by training with the unpaired data. Specifically, we adopt the generative adversarial networks and conduct the fast training in cyclic way. A new structural dissimilarity loss, which captures the detailed anatomies, is introduced to enhance the quality of the synthesized images. We validate our proposed algorithm on three popular image synthesis tasks, including brain MR-to-CT, prostate MR-to-CT, and brain 3T-to-7T. The experimental results demonstrate that our proposed method can achieve good synthesis performance by using the unpaired data only.

Original languageEnglish
Title of host publicationDeep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support - 4th International Workshop, DLMIA 2018 and 8th International Workshop, ML-CDS 2018 Held in Conjunction with MICCAI 2018
EditorsLena Maier-Hein, Tanveer Syeda-Mahmood, Zeike Taylor, Zhi Lu, Danail Stoyanov, Anant Madabhushi, João Manuel R.S. Tavares, Jacinto C. Nascimento, Mehdi Moradi, Anne Martel, Joao Paulo Papa, Sailesh Conjeti, Vasileios Belagiannis, Hayit Greenspan, Gustavo Carneiro, Andrew Bradley
PublisherSpringer Verlag
Pages155-164
Number of pages10
ISBN (Print)9783030008888
DOIs
Publication statusPublished - 2018
Externally publishedYes
Event4th International Workshop on Deep Learning in Medical Image Analysis, DLMIA 2018 and 8th International Workshop on Multimodal Learning for Clinical Decision Support, ML-CDS 2018 Held in Conjunction with MICCAI 2018 - Granada, Spain
Duration: 2018 Sep 202018 Sep 20

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11045 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other4th International Workshop on Deep Learning in Medical Image Analysis, DLMIA 2018 and 8th International Workshop on Multimodal Learning for Clinical Decision Support, ML-CDS 2018 Held in Conjunction with MICCAI 2018
CountrySpain
CityGranada
Period18/9/2018/9/20

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Xiang, L., Li, Y., Lin, W., Wang, Q., & Shen, D. (2018). Unpaired deep cross-modality synthesis with fast training. In L. Maier-Hein, T. Syeda-Mahmood, Z. Taylor, Z. Lu, D. Stoyanov, A. Madabhushi, J. M. R. S. Tavares, J. C. Nascimento, M. Moradi, A. Martel, J. P. Papa, S. Conjeti, V. Belagiannis, H. Greenspan, G. Carneiro, & A. Bradley (Eds.), Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support - 4th International Workshop, DLMIA 2018 and 8th International Workshop, ML-CDS 2018 Held in Conjunction with MICCAI 2018 (pp. 155-164). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11045 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-030-00889-5_18