Automatic segmentation of neonatal images using convex optimization and coupled level sets

Li Wang, Feng Shi, Weili Lin, John H. Gilmore, Dinggang Shen

Research output: Contribution to journalArticlepeer-review

117 Citations (Scopus)


Accurate segmentation of neonatal brain MR images remains challenging mainly due to their poor spatial resolution, inverted contrast between white matter and gray matter, and high intensity inhomogeneity. Most existing methods for neonatal brain segmentation are atlas-based and voxel-wise. Although active contour/surface models with geometric information constraint have been successfully applied to adult brain segmentation, they are not fully explored in the neonatal image segmentation. In this paper, we propose a novel neonatal image segmentation method by combining local intensity information, atlas spatial prior, and cortical thickness constraint in a single level-set framework. Besides, we also provide a robust and reliable tissue surface initialization for the proposed method by using a convex optimization technique. Thus, tissue segmentation, as well as inner and outer cortical surface reconstruction, can be obtained simultaneously. The proposed method has been tested on a large neonatal dataset, and the validation on 10 neonatal brain images (with manual segmentations) shows very promising results.

Original languageEnglish
Pages (from-to)805-817
Number of pages13
Issue number3
Publication statusPublished - 2011 Oct 1


  • Atlas-based segmentation
  • Convex optimization
  • Coupled level sets
  • Neonatal tissue segmentation

ASJC Scopus subject areas

  • Neurology
  • Cognitive Neuroscience


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