Transforming Intensity Distribution of Brain Lesions Via Conditional Gans for Segmentation

Mohammad Hamghalam, Tianfu Wang, Jing Qin, Baiying Lei

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

15 Citations (Scopus)


Brain lesion segmentation is crucial for diagnosis, surgical planning, and analysis. Owing to the fact that pixel values of brain lesions in magnetic resonance (MR) scans are distributed over the wide intensity range, there is always a considerable overlap between the class-conditional densities of lesions. Hence, an accurate automatic brain lesion segmentation is still a challenging task. We present a novel architecture based on conditional generative adversarial networks (cGANs) to improve the lesion contrast for segmentation. To this end, we propose a novel generator adaptively calibrating the input pixel values, and a Markovian discriminator to estimate the distribution of tumors. We further propose the Enhancement and Segmentation GAN (Enh-Seg-GAN) which effectively incorporates the classifier loss into the adversarial one during training to predict the central labels of the sliding input patches. Particularly, the generated synthetic MR images are a substitute for the real ones to maximize lesion contrast while suppressing the background. The potential of proposed frameworks is confirmed by quantitative evaluation compared to the state-of-the-art methods on BraTS'13 dataset.

Original languageEnglish
Title of host publicationISBI 2020 - 2020 IEEE International Symposium on Biomedical Imaging
PublisherIEEE Computer Society
Number of pages4
ISBN (Electronic)9781538693308
Publication statusPublished - Apr 2020
Event17th IEEE International Symposium on Biomedical Imaging, ISBI 2020 - Iowa City, United States
Duration: 3 Apr 20207 Apr 2020

Publication series

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


Conference17th IEEE International Symposium on Biomedical Imaging, ISBI 2020
Country/TerritoryUnited States
CityIowa City


  • Brain lesion segmentation
  • conditional GANs
  • distribution transformation
  • image synthesis
  • MRI

ASJC Scopus subject areas

  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging


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