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Mask Embedding for Realistic High-Resolution Medical Image Synthesis

  • Yinhao Ren
  • Zhe Zhu
  • Yingzhou Li
  • Dehan Kong
  • Rui Hou
  • Lars J. Grimm
  • Jeffery R. Marks
  • Joseph Y. LoEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11769)

Abstract

Generative Adversarial Networks (GANs) have found applications in natural image synthesis and begin to show promises generating synthetic medical images. In many cases, the ability to perform controlled image synthesis using masked priors such as shape and size of organs is desired. However, mask-guided image synthesis is challenging due to the pixel level mask constraint. While the few existing mask-guided image generation approaches suffer from the lack of fine-grained texture details, we tackle the issue of mask-guided stochastic image synthesis via mask embedding. Our novel architecture first encodes the input mask as an embedding vector and then inject these embedding into the random latent vector input. The intuition is to classify semantic masks into partitions before feature up-sampling for improved sample space mapping stability. We validate our approach on a large dataset containing 39,778 patients with 443,556 negative screening Full Field Digital Mammography (FFDM) images. Experimental results show that our approach can generate realistic high-resolution (\(256\times 512\)) images with pixel-level mask constraints, and outperform other state-of-the-art approaches.

Keywords

Generative Adversarial Networks Image synthesis Mask embedding Mammogram 

Notes

Acknowledgments

This work was supported in part by NIH/NCI U01-CA214183 and U2C-CA233254, and an equipment donation by NVIDIA Corporation.

Supplementary material

490281_1_En_47_MOESM1_ESM.pdf (9.2 mb)
Supplementary material 1 (pdf 9408 KB)

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Yinhao Ren
    • 1
  • Zhe Zhu
    • 2
  • Yingzhou Li
    • 3
  • Dehan Kong
    • 4
  • Rui Hou
    • 5
  • Lars J. Grimm
    • 2
  • Jeffery R. Marks
    • 6
  • Joseph Y. Lo
    • 1
    • 2
    • 5
    Email author
  1. 1.Department of Biomedical EngineeringDuke UniversityDurhamUSA
  2. 2.Department of RadiologyDuke University School of MedicineDurhamUSA
  3. 3.Department of MathematicsDuke UniversityDurhamUSA
  4. 4.Department of AutomationBeijing Institute of TechnologyBeijingChina
  5. 5.Department of Electrical EngineeringDuke UniversityDurhamUSA
  6. 6.Department of SurgeryDuke University School of MedicineDurhamUSA

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