Domain-Invariant Prior Knowledge Guided Attention Networks for Robust Skull Stripping of Developing Macaque Brains

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

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

Abstract

Non-human primates, especially macaque monkeys, with close phylogenetic relationship to humans, are highly valuable and widely used animal models for human neuroscience studies. In neuroimaging analysis of macaques, brain extraction or skull stripping of magnetic resonance imaging (MRI) is a crucial step for following processing. However, the current skull stripping methods largely focus on human brains, and thus often lead to unsatisfactory results when applying to macaque brains, especially for macaque brains during early development. In fact, the macaque brain during infancy undergoes regionally-heterogeneous dynamic development, leading to poor and age-variable contrasts between different anatomical structures, posing great challenges for accurate skull stripping. In this study, we propose a novel framework to effectively combine intensity information and domain-invariant prior knowledge, which are important guidance information for accurate brain extraction of developing macaques from 0 to 36 months of age. Specifically, we introduce signed distance map (SDM) and center of gravity distance map (CGDM) based on the intermediate segmentation results and fuse their information by Dual Self-Attention Module (DSAM) instead of local convolution. To evaluate the performance, we adopt two large-scale and challenging MRI datasets from rhesus macaques and cynomolgus macaques, respectively, with totally 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. Experimental results show the robustness of our plug-and-play method on cross-source MRI datasets without any transfer learning.

Original languageEnglish
Title of host publicationMedical Image Computing and Computer Assisted Intervention – MICCAI 2020 - 23rd International Conference, Proceedings
EditorsAnne L. Martel, Purang Abolmaesumi, Danail Stoyanov, Diana Mateus, Maria A. Zuluaga, S. Kevin Zhou, Daniel Racoceanu, Leo Joskowicz
PublisherSpringer Science and Business Media Deutschland GmbH
Pages22-32
Number of pages11
ISBN (Print)9783030597276
DOIs
Publication statusPublished - 2020
Event23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020 - Lima, Peru
Duration: 2020 Oct 42020 Oct 8

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12267 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020
CountryPeru
CityLima
Period20/10/420/10/8

Keywords

  • Dual self-attention
  • Macaques skull stripping
  • Prior knowledge

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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