Deep 2D Encoder-Decoder Convolutional Neural Network for Multiple Sclerosis Lesion Segmentation in Brain MRI

  • Shahab AslaniEmail author
  • Michael Dayan
  • Vittorio Murino
  • Diego Sona
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11383)


In this paper, we propose an automated segmentation approach based on a deep two-dimensional fully convolutional neural network to segment brain multiple sclerosis lesions from multimodal magnetic resonance images. The proposed model is made as a combination of two deep subnetworks. An encoding network extracts different feature maps at various resolutions. A decoding part upconvolves the feature maps combining them through shortcut connections during an upsampling procedure. To the best of our knowledge, the proposed model is the first slice-based fully convolutional neural network for the purpose of multiple sclerosis lesion segmentation. We evaluated our network on a freely available dataset from ISBI MS challenge with encouraging results from a clinical perspective.


Segmentation Multiple sclerosis Convolutional neural network 



We respectfully acknowledge NVIDIA for GPU donation.


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Shahab Aslani
    • 1
    • 2
    Email author
  • Michael Dayan
    • 1
  • Vittorio Murino
    • 1
    • 3
  • Diego Sona
    • 1
    • 4
  1. 1.Pattern Analysis and Computer Vision (PAVIS)Istituto Italiano di Tecnologia (IIT)GenoaItaly
  2. 2.Science and Technology for Electronic and Telecommunication EngineeringUniversity of GenoaGenoaItaly
  3. 3.Dipartimento di InformaticaUniversity of VeronaVeronaItaly
  4. 4.NeuroInformatics LaboratoryFondazione Bruno KesslerTrentoItaly

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