Automatic segmentation of eight tissue classes in neonatal brain MRI

PLoS One. 2013 Dec 17;8(12):e81895. doi: 10.1371/journal.pone.0081895. eCollection 2013.


Purpose: Volumetric measurements of neonatal brain tissues may be used as a biomarker for later neurodevelopmental outcome. We propose an automatic method for probabilistic brain segmentation in neonatal MRIs.

Materials and methods: In an IRB-approved study axial T1- and T2-weighted MR images were acquired at term-equivalent age for a preterm cohort of 108 neonates. A method for automatic probabilistic segmentation of the images into eight cerebral tissue classes was developed: cortical and central grey matter, unmyelinated and myelinated white matter, cerebrospinal fluid in the ventricles and in the extra cerebral space, brainstem and cerebellum. Segmentation is based on supervised pixel classification using intensity values and spatial positions of the image voxels. The method was trained and evaluated using leave-one-out experiments on seven images, for which an expert had set a reference standard manually. Subsequently, the method was applied to the remaining 101 scans, and the resulting segmentations were evaluated visually by three experts. Finally, volumes of the eight segmented tissue classes were determined for each patient.

Results: The Dice similarity coefficients of the segmented tissue classes, except myelinated white matter, ranged from 0.75 to 0.92. Myelinated white matter was difficult to segment and the achieved Dice coefficient was 0.47. Visual analysis of the results demonstrated accurate segmentations of the eight tissue classes. The probabilistic segmentation method produced volumes that compared favorably with the reference standard.

Conclusion: The proposed method provides accurate segmentation of neonatal brain MR images into all given tissue classes, except myelinated white matter. This is the one of the first methods that distinguishes cerebrospinal fluid in the ventricles from cerebrospinal fluid in the extracerebral space. This method might be helpful in predicting neurodevelopmental outcome and useful for evaluating neuroprotective clinical trials in neonates.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Brain / anatomy & histology*
  • Female
  • Humans
  • Image Processing, Computer-Assisted / methods*
  • Infant, Newborn
  • Infant, Premature
  • Magnetic Resonance Imaging / methods*
  • Male
  • Nerve Fibers, Myelinated*
  • Organ Size
  • Pattern Recognition, Automated / methods*

Grant support

This work was supported by the Young Investigator Exchange Program Fellowship of the International Pediatric Research Foundation and The Netherlands Organisation for Health Research and Development [project 94527022]. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.