Deep learning deformation initialization for rapid groupwise registration of inhomogeneous image populations

Sahar Ahmad, Jingfan Fan, Pei Dong, Xiaohuan Cao, Pew Thian Yap, Dinggang Shen

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

Groupwise image registration tackles biases that can potentially arise from inappropriate template selection. It typically involves simultaneous registration of a cohort of images to a common space that is not specified a priori. Existing groupwise registration methods are computationally complex and are only effective for image populations without large anatomical variations. In this paper, we propose a deep learning framework to rapidly estimate large deformations between images to significantly reduce structural variability. Specifically, we employ a multi-level graph coarsening method to agglomerate similar images into clusters, each represented by an exemplar image. We then use a deep learning framework to predict the initial deformations between images. Warping with the estimated deformations brings the images closer in the image manifold and their alignment can be further refined using conventional groupwise registration algorithms. We evaluated the effectiveness of our method in groupwise registration of MR brain images and compared it against state-of-the-art groupwise registration methods. Experimental results indicate that deformation initialization enables groupwise registration to converge significantly faster with competitive accuracy, therefore facilitates large-scale imaging studies.

Original languageEnglish
Article number34
JournalFrontiers in Neuroinformatics
Volume13
DOIs
Publication statusPublished - 2019 Apr 16

Keywords

  • Brain templates
  • Convolutional neural network
  • Deep learning
  • Graph coarsening
  • Groupwise registration
  • MRI

ASJC Scopus subject areas

  • Neuroscience (miscellaneous)
  • Biomedical Engineering
  • Computer Science Applications

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