Pediatric Brain Tissue Segmentation from MRI using Clustering: a Preliminary Study

Brain Tissue Segmentation (BTS) in young children and neonates is not a trivial task due to peculiar characteristics of the developing brain. The aim of this study is to present the preliminary results of new atlas-free BTS (afBTS) algorithm of MR images for pediatric applications, based on clustering. The algorithm works on axial T1, T2 and FLAIR sequences. First, the Cerebrospinal Fluid (CSF) is identified using the Region Growing algorithm. The remaining voxels are processed with the k-means algorithm in order to separate White Matter (WM) and Grey Matter (GM). The afBTS algorithm was applied to a population of 13 neonates; the segmentations were evaluated by two expert pediatric neuroradiologists and compared with an atlas-based algorithm. The results were promising: afBTS allowed reconstruction of WM and CSF with an image quality comparable to the reference of standard while lower segmentation quality was obtained for the GM segmentation.

• Publication year

  2019

• Venue

  Annual International Conference of the IEEE Engineering in Medicine and Biology Society

• Publication date

  2019-07-01

• Fields of study

  Medicine, Computer Science

• Identifiers
  DOI 10.1109/EMBC.2019.8856697PMID 31947344
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar, PubMed

• No claims are published for this paper.

• No concepts are published for this paper.

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