• Ying Zhu, Geoffrey S Young, Zhong Xue, Raymond Y Huang, Hui You, Kian Setayesh, Hiroto Hatabu, Fei Cao, and Stephen T Wong.
• Department of Systems Medicine and Bioengineering, The Methodist Hospital Research Institute, Weill Cornell Medical College, Houston, TX, USA.
• Acad Radiol. 2012 Aug 1; 19 (8): 977-85.

Rationale And ObjectivesQuantitative measurement provides essential information about disease progression and treatment response in patients with glioblastoma multiforme (GBM). The goal of this article is to present and validate a software pipeline for semi-automatic GBM segmentation, called AFINITI (Assisted Follow-up in NeuroImaging of Therapeutic Intervention), using clinical data from GBM patients.Materials And MethodsOur software adopts the current state-of-the-art tumor segmentation algorithms and combines them into one clinically usable pipeline. Both the advantages of the traditional voxel-based and the deformable shape-based segmentation are embedded into the software pipeline. The former provides an automatic tumor segmentation scheme based on T1- and T2-weighted magnetic resonance (MR) brain data, and the latter refines the segmentation results with minimal manual input.ResultsTwenty-six clinical MR brain images of GBM patients were processed and compared with manual results. The results can be visualized using the embedded graphic user interface.ConclusionValidation results using clinical GBM data showed high correlation between the AFINITI results and manual annotation. Compared to the voxel-wise segmentation, AFINITI yielded more accurate results in segmenting the enhanced GBM from multimodality MR imaging data. The proposed pipeline could be used as additional information to interpret MR brain images in neuroradiology.Copyright © 2012 AUR. Published by Elsevier Inc. All rights reserved.

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