DIKA-Nets: Domain-invariant knowledge-guided attention networks for brain skull stripping of early developing macaques

Tao Zhong, Fenqiang Zhao, Yuchen Pei, Zhenyuan Ning, Lufan Liao, Zhengwang Wu, Yuyu Niu, Li Wang, Dinggang Shen, Yu Zhang, Gang Li

Research output: Contribution to journalArticlepeer-review

17 Citations (Scopus)


As non-human primates, macaques have a close phylogenetic relationship to human beings and have been proven to be a valuable and widely used animal model in human neuroscience research. Accurate skull stripping (aka. brain extraction) of brain magnetic resonance imaging (MRI) is a crucial prerequisite in neuroimaging analysis of macaques. Most of the current skull stripping methods can achieve satisfactory results for human brains, but when applied to macaque brains, especially during early brain development, the results are often unsatisfactory. In fact, the early dynamic, regionally-heterogeneous development of macaque brains, accompanied by poor and age-related contrast between different anatomical structures, poses significant challenges for accurate skull stripping. To overcome these challenges, we propose a fully-automated framework to effectively fuse the age-specific intensity information and domain-invariant prior knowledge as important guiding information for robust skull stripping of developing macaques from 0 to 36 months of age. Specifically, we generate Signed Distance Map (SDM) and Center of Gravity Distance Map (CGDM) based on the intermediate segmentation results as guidance. Instead of using local convolution, we fuse all information using the Dual Self-Attention Module (DSAM), which can capture global spatial and channel-dependent information of feature maps. To extensively evaluate the performance, we adopt two relatively-large challenging MRI datasets from rhesus macaques and cynomolgus macaques, respectively, with a total of 361 scans from two different scanners with different imaging protocols. We perform cross-validation by using one dataset for training and the other one for testing. Our method outperforms five popular brain extraction tools and three deep-learning-based methods on cross-source MRI datasets without any transfer learning.

Original languageEnglish
Article number117649
Publication statusPublished - 2021 Feb 15

Bibliographical note

Publisher Copyright:
© 2020


  • Dual self-attention
  • Infant macaques
  • Prior knowledge
  • Skull stripping

ASJC Scopus subject areas

  • Neurology
  • Cognitive Neuroscience


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