A New Multi-Atlas Registration Framework for Multimodal Pathological Images Using Conventional Monomodal Normal Atlases

Zhenyu Tang, Pew Thian Yap, Dinggang Shen

Research output: Contribution to journalArticlepeer-review

26 Citations (Scopus)


Using multi-atlas registration (MAR), information carried by atlases can be transferred onto a new input image for the tasks of region-of-interest (ROI) segmentation, anatomical landmark detection, and so on. Conventional atlases used in MAR methods are monomodal and contain only normal anatomical structures. Therefore, the majority of MAR methods cannot handle input multimodal pathological images, which are often collected in routine image-based diagnosis. This is because registering monomodal atlases with normal appearances to multimodal pathological images involves two major problems: 1) missing imaging modalities in the monomodal atlases and 2) influence from pathological regions. In this paper, we propose a new MAR framework to tackle these problems. In this framework, deep learning-based image synthesizers are applied for synthesizing multimodal normal atlases from conventional monomodal normal atlases. To reduce the influence from pathological regions, we further propose a multimodal low-rank approach to recover multimodal normal-looking images from multimodal pathological images. Finally, the multimodal normal atlases can be registered to the recovered multimodal images in a multi-channel way. We evaluate our MAR framework via brain ROI segmentation of multimodal tumor brain images. Due to the utilization of multimodal information and the reduced influence from pathological regions, experimental results show that registration based on our method is more accurate and robust, leading to significantly improved brain ROI segmentation compared with the state-of-the-art methods.

Original languageEnglish
Article number2884563
Pages (from-to)2293-2304
Number of pages12
JournalIEEE Transactions on Image Processing
Issue number5
Publication statusPublished - 2019 May

Bibliographical note

Funding Information:
Manuscript received May 10, 2018; revised October 22, 2018; accepted November 27, 2018. Date of publication December 17, 2018; date of current version January 30, 2019. This work was supported in part by the National Institutes of Health (NIH) under Grant AG053867, Grant EB006733, and Grant EB008374 and in part by the National Natural Science Foundation of China under Grant 61502002. The associate editor coordinating the review of this manuscript and approving it for publication was Dr. Jan Sijbers. (Corresponding author: Dinggang Shen.) Z. Tang is with the Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599 USA, and also with the School of Computer Science and Technology, Anhui University, Hefei 230601, China.

Publisher Copyright:
© 1992-2012 IEEE.


  • Image registration
  • image synthesis
  • low-rank image recovery
  • multimodal image
  • pathological brain image

ASJC Scopus subject areas

  • Software
  • Computer Graphics and Computer-Aided Design


Dive into the research topics of 'A New Multi-Atlas Registration Framework for Multimodal Pathological Images Using Conventional Monomodal Normal Atlases'. Together they form a unique fingerprint.

Cite this