title: Automatic segmentation, internal classification, and follow-up of optic pathway gliomas in MRI.
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.
authors.affiliation: School of Eng. and Computer Science, The Hebrew University of Jerusalem, Israel. lweizm45@cs.huji.ac.il
authors.forename: L
authors.initials: L
authors.lastname: Weizman
authors.forename: L
authors.initials: L
authors.lastname: Ben Sira
authors.forename: L
authors.initials: L
authors.lastname: Joskowicz
authors.forename: S
authors.initials: S
authors.lastname: Constantini
authors.forename: R
authors.initials: R
authors.lastname: Precel
authors.forename: B
authors.initials: B
authors.lastname: Shofty
authors.forename: D
authors.initials: D
authors.lastname: Ben Bashat
mesh_terms: D000465:Algorithms; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D009887:Ophthalmoscopy / Q000379:methods*; D009900:Optic Nerve / Q000473:pathology*; D020339:Optic Nerve Glioma / Q000473:pathology*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity