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Abstract #2250

Novel Method for Automatic Segmentation of Infiltrative Glioblastoma

Kelvin Wong 1,2 and Stephen Wong 1,2

1 Department of Systems Medicine and Bioengineering, Houston Methodist Research Institute, Houston, TX, United States, 2 Department of Radiology, Weill Cornell Medical College, New York, NY, United States

Glioblastoma Multiforme (GBM) is the most lethal and common brain cancer in adult. Our goal is to quantitatively extract the infiltrating tumor information from imaging. Infiltrative tumor is with low Gd-enhancement and is difficult to identify. To investigate the prevalence and extent of low Gd-enhancement tumor in GBM, we developed an algorithm to automatically segment the low Gd-enhancement region. The method is applied to the GBM collection in The Cancer Imaging Archive (TCIA). The proposed algorithm can robustly segment different components of the tumor including low Gd-enhancement region.

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