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