International Conference on Medical Image Computing and Computer-Assisted Intervention

MICCAI 2015: Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2015 pp 69-76 | Cite as

Prediction of Motor Function in Very Preterm Infants Using Connectome Features and Local Synthetic Instances

  • Colin J. Brown
  • Steven P. Miller
  • Brian G. Booth
  • Kenneth J. Poskitt
  • Vann Chau
  • Anne R. Synnes
  • Jill G. Zwicker
  • Ruth E. Grunau
  • Ghassan Hamarneh
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9349)

Abstract

We propose a method to identify preterm infants at highest risk of adverse motor function (identified at 18 months of age) using connectome features from a diffusion tensor image (DTI) acquired shortly after birth. For each full-brain DTI, a connectome is constructed and network features are extracted. After further reducing the dimensionality of the feature vector via PCA, SVM is used to discriminate between normal and abnormal motor scores. We further introduce a novel method to produce realistic synthetic training data in order to reduce the effects of class imbalance. Our method is tested on a dataset of 168 DTIs of 115 very preterm infants, scanned between 27 and 45 weeks post-menstrual age. We show that using our synthesized training data can consistently improve classification accuracy while setting a baseline for this challenging prediction problem. This work presents the first image analysis approach to predicting impairment in motor function in preterm-born infants.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Colin J. Brown
    • 1
  • Steven P. Miller
    • 2
  • Brian G. Booth
    • 1
  • Kenneth J. Poskitt
    • 3
  • Vann Chau
    • 2
  • Anne R. Synnes
    • 3
  • Jill G. Zwicker
    • 3
  • Ruth E. Grunau
    • 3
  • Ghassan Hamarneh
    • 1
  1. 1.Simon Fraser UniversityBurnabyCanada
  2. 2.The Hospital for Sick Children and The University of TorontoTorontoCanada
  3. 3.University of British Columbia and Child and Family Research InstituteVancouverCanada

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