DA-GAN: Learning structured noise removal in ultrasound volume projection imaging for enhanced spine segmentation

Zixun Huang, Rui Zhao, Frank H.F. Leung, Kin Man Lam, Sai Ho Ling, Juan Lyu, Sunetra Banerjee, Timothy Tin Yan Lee, De Yang, Yong Ping Zheng

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review


Ultrasound volume projection imaging (VPI) has shown to be appealing from a clinical perspective, because of its harmlessness, flexibility, and efficiency in scoliosis assessment. However, the limitations in hardware devices degrade the resultant image content with strong structured noise. Owing to the unavailability of reference data and the unpredictable degradation model, VPI image recovery is a challenging problem. In this paper, we propose a novel framework to learn the structured noise removal from unpaired samples. We introduce the attention mechanism into the generative adversarial network to enhance the learning by focusing on the salient corrupted patterns. We also present a dual adversarial learning strategy and integrate the denoiser with a segmentation model to produce the task-oriented noiseless estimation. Experimental results show that the proposed method can improve both the visual quality and the segmentation accuracy on spine images.

Original languageEnglish
Title of host publication2021 IEEE 18th International Symposium on Biomedical Imaging, ISBI 2021
PublisherIEEE Computer Society
Number of pages5
ISBN (Electronic)9781665412469
Publication statusPublished - 13 Apr 2021
Event18th IEEE International Symposium on Biomedical Imaging, ISBI 2021 - Nice, France
Duration: 13 Apr 202116 Apr 2021

Publication series

NameProceedings - International Symposium on Biomedical Imaging
ISSN (Print)1945-7928
ISSN (Electronic)1945-8452


Conference18th IEEE International Symposium on Biomedical Imaging, ISBI 2021


  • Spine segmentation
  • Ultrasound image restoration
  • Unpaired learning

ASJC Scopus subject areas

  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging

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