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

Artif Intell Med. 2020 Jan:102:101769. doi: 10.1016/j.artmed.2019.101769. Epub 2019 Nov 27.


Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) plays an important role in diagnosis and grading of brain tumors. 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 errors. 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 and clinical 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 while requiring less than 3 min to process an entire input DCE-MRI study using a single GPU.

Keywords: Brain; DCE-MRI; Deep neural network; Perfusion; Pharmacokinetic model; Tumor segmentation.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Automation
  • Brain Neoplasms / blood supply
  • Brain Neoplasms / diagnostic imaging*
  • Contrast Media* / pharmacokinetics
  • Databases, Factual
  • Deep Learning*
  • Humans
  • Magnetic Resonance Imaging / methods*
  • Phantoms, Imaging
  • Pharmacokinetics
  • Prognosis
  • Regional Blood Flow
  • Reproducibility of Results
  • Sensitivity and Specificity


  • Contrast Media