Frnet: Flattened residual network for infant MRI skull stripping

Qian Zhang, Li Wang, Xiaopeng Zong, Weili Lin, Gang Li, Dinggang Shen

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

18 Citations (Scopus)


Skull stripping for brain MR images is a basic segmentation task. Although many methods have been proposed, most of them focused mainly on the adult MR images. Skull strip-ping for infant MR images is more challenging due to the small size and dynamic intensity changes of brain tissues during the early ages. In this paper, we propose a novel CNN based framework to robustly extract brain region from infant MR image without any human assistance. Specifically, we propose a simplified but more robust flattened residual net-work architecture (FRnet). We also introduce a new boundary loss function to highlight ambiguous and low contrast regions between brain and non-brain regions. To make the whole framework more robust to MR images with different imaging quality, we further introduce an artifact simulator for data augmentation. We have trained and tested our proposed framework on a large dataset (N=343), covering newborns to 48-month-olds, and obtained performance better than the state-of-the-art methods in all age groups.

Original languageEnglish
Title of host publicationISBI 2019 - 2019 IEEE International Symposium on Biomedical Imaging
PublisherIEEE Computer Society
Number of pages4
ISBN (Electronic)9781538636411
Publication statusPublished - 2019 Apr
Event16th IEEE International Symposium on Biomedical Imaging, ISBI 2019 - Venice, Italy
Duration: 2019 Apr 82019 Apr 11

Publication series

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


Conference16th IEEE International Symposium on Biomedical Imaging, ISBI 2019

Bibliographical note

Publisher Copyright:
© 2019 IEEE.


  • Deep learning
  • Infant brain
  • Skull stripping

ASJC Scopus subject areas

  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging


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