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Abstract #3749

Brain Tumor Characterization and Assessment using Automatic Detection of Extracellular pH Change

Yuki Matsumoto1, Masafumi Harada1, Yuki Kanazawa1, Nagomi Fukuda2, Syun Kitano2, Yo Taniguchi3, Masaharu Ono3, and Yoshitaka Bito3
1Graduate School of Biomedical Sciences, Tokushima University, Tokushima-city, Japan, 2School of Health Sciences, Tokushima University, Tokushima-city, Japan, 3Healthcare Business Unit, Hitachi, Ltd., Tokyo, Japan

In this study, we attempted to characterize brain tumors by combining quantitative parameter mapping and deep-learning-based semantic segmentation. T1, concentration of contrast media (CM), and pHe maps were calculated after the image dataset was obtained. The contrast-enhanced area was then automatically detected using a deep-learning-based semantic segmentation algorithm. The segmented mask was set as the region of interest on these calculated maps. The statistical significance of differences in brain tumors was evaluated to determine whether changes in the mean T1, CM, and pHe were malignancy-dependent.

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