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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