LABEL: Pediatric brain extraction using learning-based meta-algorithm

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

Research output: Contribution to journalArticle

115 Citations (Scopus)

Abstract

Magnetic resonance imaging of pediatric brain provides valuable information for early brain development studies. Automated brain extraction is challenging due to the small brain size and dynamic change of tissue contrast in the developing brains. In this paper, we propose a novel Learning Algorithm for Brain Extraction and Labeling (LABEL) specially for the pediatric MR brain images. The idea is to perform multiple complementary brain extractions on a given testing image by using a meta-algorithm, including BET and BSE, where the parameters of each run of the meta-algorithm are effectively learned from the training data. Also, the representative subjects are selected as exemplars and used to guide brain extraction of new subjects in different age groups. We further develop a level-set based fusion method to combine multiple brain extractions together with a closed smooth surface for obtaining the final extraction. The proposed method has been extensively evaluated in subjects of three representative age groups, such as neonate (less than 2. months), infant (1-2. years), and child (5-18. years). Experimental results show that, with 45 subjects for training (15 neonates, 15 infant, and 15 children), the proposed method can produce more accurate brain extraction results on 246 testing subjects (75 neonates, 126 infants, and 45 children), i.e., at average Jaccard Index of 0.953, compared to those by BET (0.918), BSE (0.902), ROBEX (0.901), GCUT (0.856), and other fusion methods such as Majority Voting (0.919) and STAPLE (0.941). Along with the largely-improved computational efficiency, the proposed method demonstrates its ability of automated brain extraction for pediatric MR images in a large age range.

Original languageEnglish
Pages (from-to)1975-1986
Number of pages12
JournalNeuroImage
Volume62
Issue number3
DOIs
Publication statusPublished - 2012 Sep 1

Keywords

  • Affinity propagation
  • Brain extraction
  • Infant brain analysis
  • Label fusion
  • Level-set
  • Meta-algorithm
  • Skull stripping

ASJC Scopus subject areas

  • Neurology
  • Cognitive Neuroscience

Fingerprint Dive into the research topics of 'LABEL: Pediatric brain extraction using learning-based meta-algorithm'. Together they form a unique fingerprint.

  • Cite this