Automated segmentation of mouse brain images using multi-atlas multi-ROI deformation and label fusion

Jingxin Nie, Dinggang Shen

Research output: Contribution to journalArticlepeer-review

22 Citations (Scopus)


We propose an automated multi-atlas and multi-ROI based segmentation method for both skull-stripping of mouse brain and the ROI-labeling of mouse brain structures from the three dimensional (3D) magnetic resonance images (MRI). Three main steps are involved in our method. First, a region of interest (ROI) guided warping algorithm is designed to register multi-atlas images to the subject space, by considering more on the matching of image contents around the ROI boundaries which are more important for ROI labeling. Then, a multi-atlas and multi-ROI based deformable segmentation method is adopted to refine the ROI labeling result by deforming each ROI surface via boundary recognizers (i.e.; SVM classifiers) trained on local surface patches. Finally, a local-mutual-information (MI) based multi-label fusion technique is proposed for allowing the atlases with better local image similarity with the subject to have more contributions in label fusion. The experimental results show that our method works better than the conventional methods on both in vitro and in vivo mouse brain datasets.

Original languageEnglish
Pages (from-to)35-45
Number of pages11
Issue number1
Publication statusPublished - 2013 Jan

Bibliographical note

Funding Information:
Acknowledgment This work was supported in part by NIH grants EB006733, EB008374, EB009634, AG041721, and CA140413, by National Science Foundation of China under grant No. 61075010, and also by The National Basic Research Program of China (973 Program) grant No. 2010CB732505.


  • Deformable segmentation
  • Label fusion
  • Mouse brain images
  • Multi-ROIs
  • Multi-atlases
  • Segmentation

ASJC Scopus subject areas

  • Software
  • General Neuroscience
  • Information Systems


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