  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.
  Methods: The developed DeepSeg is a modular decoupling framework. It consists
of two connected core parts based on an encoding and decoding relationship. The
encoder part is a convolutional neural network (CNN) responsible for spatial
information extraction. The resulting semantic map is inserted into the decoder
part to get the full resolution probability map. Based on modified U-Net
architecture, different CNN models such as Residual Neural Network (ResNet),
Dense Convolutional Network (DenseNet), and NASNet have been utilized in this
study.
  Results: The proposed deep learning architectures have been successfully
tested and evaluated on-line based on MRI datasets of Brain Tumor Segmentation
(BraTS 2019) challenge, including s336 cases as training data and 125 cases for
validation data. The dice and Hausdorff distance scores of obtained
segmentation results are about 0.81 to 0.84 and 9.8 to 19.7 correspondingly.
  Conclusion: This study showed successful feasibility and comparative
performance of applying different deep learning models in a new DeepSeg
framework for automated brain tumor segmentation in FLAIR MR images. The
proposed DeepSeg is open-source and freely available at
https://github.com/razeineldin/DeepSeg/.
