Perceptual data augmentation for biomedical coronary vessel segmentation

Yuxin Ma*, Shuo Wang, Yang Hua, Ruhui Ma*, Tao Song, Zhengui Xue, Heng Cao, Haibing Guan

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)

Abstract

Sufficient annotated data is critical to the success of deep learning methods. Annotating for vessel segmentation in X-ray coronary angiograms is extremely difficult because of the small and complex structures to be processed. Although unsupervised domain adaptation methods can be utilized to alleviate the annotation burden by using data in other domains, e.g., eye fundus images, these methods cannot perform well due to the characteristic of medical images. Data augmentation can help improve the similarity of source domain and target domain in unsupervised domain adaptation tasks. Existing data augmentation methods play a limited role in improving domain adaptation performance, especially for special medical image segmentation tasks. In this paper, we propose an effective perceptual data augmentation method to improve the similarity between eye fundus images and coronary angiograms by synthesizing virtual samples. Auto Foreground Augment method is designed to search for geometric transformations that improve the similarity between foreground vessels of eye fundus images and coronary angiograms. The Haar Wavelet-Based Perceptual Similarity Index is utilized to guide the synthesis of virtual samples in foreground and background mixup. Extensive experiments show that our data augmentation method can synthesize high-quality virtual samples and thus improve the domain adaptation performance. To our best knowledge, this is the first work to apply perceptual data augmentation to vessel segmentation in coronary angiograms.

Original languageEnglish
Pages (from-to)2494-2505
Number of pages12
JournalIEEE/ACM Transactions on Computational Biology and Bioinformatics
Volume20
Issue number4
Early online date04 Jul 2022
DOIs
Publication statusPublished - Jul 2023

Keywords

  • Coronary angiograms
  • data augmentation
  • domain adaptation
  • intelligent processing
  • vessel segmentation

ASJC Scopus subject areas

  • Biotechnology
  • Genetics
  • Applied Mathematics

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