Fully-automated deep learning-powered system for DCE-MRI analysis of brain tumors
This addresses the time-consuming and error-prone manual analysis for clinicians diagnosing brain tumors, though it appears incremental as it builds on existing deep learning and pharmacokinetic modeling methods.
The paper tackles the problem of automating DCE-MRI analysis for brain tumors, proposing a fully-automated deep learning system that achieves state-of-the-art results in segmentation accuracy and contrast-concentration fitting, processing an entire study in under 3 minutes on a single GPU.
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.