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

• Published in: Medical image analysis 2012-01, Vol.16 (1), p.177-188
• Main Authors: Weizman, L., Ben Sira, L., Joskowicz, L., Constantini, S., Precel, R., Shofty, B., Ben Bashat, D.
• Format: Article
• Language: English
• Subjects: Algorithms; Brain tumor; Humans; Image Enhancement - methods; Image Interpretation, Computer-Assisted - methods; Magnetic Resonance Imaging - methods; Multi-sequence MRI; Neurofibromatosis; Ophthalmoscopy - methods; Optic Nerve - pathology; Optic Nerve Glioma - pathology; Optic pathway glioma; Pattern Recognition, Automated - methods; Reproducibility of Results; Segmentation; Sensitivity and Specificity
• ISSN: 1361-8415; 1361-8423
• DOI: 10.1016/j.media.2011.07.001

[Display omitted] ►Brain tumor segmentation based on prior tumor location information. ► OPG components classification without grey-level normalization. ► OPG follow-up that supports automatic monitoring of disease progression. ► Evaluation and follow-up with a robust and consistent measurement tool. 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 0.73mm 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.