Neonatal brain MRI segmentation by building multi-region-multireference atlases

  • Authors:

  • Feng Shi;Pew-Thian Yap;Yong Fan;John H. Gilmore;Weili Lin;Dinggang Shen

  • Affiliations:

  • IDEA Lab, Department of Radiology and BRIC, University of North Carolina at Chapel Hill;IDEA Lab, Department of Radiology and BRIC, University of North Carolina at Chapel Hill;IDEA Lab, Department of Radiology and BRIC, University of North Carolina at Chapel Hill;Department of Psychiatry, University of North Carolina at Chapel Hill;MRI Lab, Department of Radiology and BRIC, University of North Carolina at Chapel Hill;IDEA Lab, Department of Radiology and BRIC, University of North Carolina at Chapel Hill

  • Venue:
  • ISBI'10 Proceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to Macro
  • Year:
  • 2010

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Abstract

Neonatal brain MRI segmentation is challenging due to the poor image quality. Existing population atlases used for guiding segmentation are usually constructed by averaging all images in a population with no preference. However, such approaches diminish the important local inter-subject structural variability. In this paper, we propose a multiregion-multi-reference strategy for atlas building from a population. In brief, the brain is first parcellated into multiple anatomical regions, and for each region, the population images are classified into different subpopulations. The exemplars in Sub-populations serve as structural references when determining the most suitable regional atlas for a to-be-segmented image. A final atlas is generated by combining all selected regional atlases, and a joint registration-segmentation strategy is employed for tissue segmentation. Experimental results demonstrate that segmentation with our atlas achieves high average tissue overlap rates with manual golden standard of 0.86 (SD 0.02) for gray matter (GM) and 0.83 (SD 0.03) for white matter (WM), and outperforms other atlases in comparison.