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Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) plays animportant role in diagnosis and grading of brain tumor. Although manual DCEbiomarker extraction algorithms boost the diagnostic yield of DCE-MRI byproviding quantitative information on tumor prognosis and prediction, they aretime-consuming and prone to human error. In this paper, we propose afully-automated, end-to-end system for DCE-MRI analysis of brain tumors. Ourdeep learning-powered technique does not require any user interaction, ityields reproducible results, and it is rigorously validated against benchmark(BraTS'17 for tumor segmentation, and a test dataset released by theQuantitative Imaging Biomarkers Alliance for the contrast-concentrationfitting) and clinical (44 low-grade glioma patients) data. Also, we introduce acubic model of the vascular input function used for pharmacokinetic modelingwhich significantly decreases the fitting error when compared with the state ofthe art, alongside a real-time algorithm for determination of the vascularinput region. An extensive experimental study, backed up with statisticaltests, showed that our system delivers state-of-the-art results (in terms ofsegmentation accuracy and contrast-concentration fitting) while requiring lessthan 3 minutes to process an entire input DCE-MRI study using a single GPU.

Fully-automated deep learning-powered system for DCE-MRI analysis of brain tumors