Automatic segmentation, internal classification, and follow-up of optic pathway gliomas in MRI

L. Weizman*, L. Ben Sira, L. Joskowicz, S. Constantini, R. Precel, B. Shofty, D. Ben Bashat

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

67 Scopus citations

Abstract

This paper presents an automatic method for the segmentation, internal classification and follow-up of optic pathway gliomas (OPGs) from multi-sequence MRI datasets. Our method starts with the automatic localization of the OPG and its core with an anatomical atlas followed by a binary voxel classification with a probabilistic tissue model whose parameters are estimated from the MR images. The method effectively incorporates prior location, tissue characteristics, and intensity information for the delineation of the OPG boundaries in a consistent and repeatable manner. Internal classification of the segmented OPG volume is then obtained with a robust method that overcomes grey-level differences between learning and testing datasets. Experimental results on 25 datasets yield a mean surface distance error of 0.73. mm as compared to manual segmentation by experienced radiologists. Our method exhibits reliable performance in OPG growth follow-up MR studies, which are crucial for monitoring disease progression. To the best of our knowledge, this is the first method that addresses automatic segmentation, internal classification, and follow-up of OPG.

Original languageAmerican English
Pages (from-to)177-188
Number of pages12
JournalMedical Image Analysis
Volume16
Issue number1
DOIs
StatePublished - Jan 2012

Keywords

  • Brain tumor
  • Multi-sequence MRI
  • Neurofibromatosis
  • Optic pathway glioma
  • Segmentation

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