DeepSeg: deep neural network framework for automatic brain tumor segmentation using magnetic resonance FLAIR images.

Journal: International journal of computer assisted radiology and surgery
Published Date: May 5, 2020

Abstract

PURPOSE: Gliomas are the most common and aggressive type of brain tumors due to their infiltrative nature and rapid progression. The process of distinguishing tumor boundaries from healthy cells is still a challenging task in the clinical routine. Fluid-attenuated inversion recovery (FLAIR) MRI modality can provide the physician with information about tumor infiltration. Therefore, this paper proposes a new generic deep learning architecture, namely DeepSeg, for fully automated detection and segmentation of the brain lesion using FLAIR MRI data.

Authors

  • Ramy A Zeineldin
    Research Group Computer Assisted Medicine (CaMed), Reutlingen University, 72762, Reutlingen, Germany. Ramy.Zeineldin@Reutlingen-University.DE.
  • Mohamed E Karar
    Faculty of Electronic Engineering (FEE), Menoufia University, Menouf, 32952, Egypt.
  • Jan Coburger
    Department of Neurosurgery, University of Ulm, 89312, Günzburg, Germany.
  • Christian R Wirtz
    Department of Neurosurgery, University of Ulm, 89312, Günzburg, Germany.
  • Oliver Burgert
    Research Group Computer Assisted Medicine (CaMed), Reutlingen University, 72762, Reutlingen, Germany.

Keywords

External Resources

Popular Topics
Recent Journals