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
Affiliations
Tao Zhong
Guangdong Provincial Key Laboratory of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou, China; Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, USA
Fenqiang Zhao
Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, USA
Yuchen Pei
Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, USA
Zhenyuan Ning
Guangdong Provincial Key Laboratory of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou, China; Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, USA
Lufan Liao
Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, USA
Zhengwang Wu
Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, USA
Yuyu Niu
Yunnan Key Laboratory of Primate Biomedical Research, Institute of Primate Translational Medicine, Kunming University of Science and Technology, Kunming, China
Li Wang
Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, USA
Dinggang Shen
Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, USA
Yu Zhang
Guangdong Provincial Key Laboratory of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou, China; Corresponding author at: Guangdong Provincial Key Laboratory of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou, China.
Gang Li
Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, USA; Corresponding author at: Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, USA.
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.
