Ischemic Stroke Lesion Segmentation Using Adversarial Learning

  • Mobarakol Islam
  • N. Rajiv Vaidyanathan
  • V. Jeya Maria Jose
  • Hongliang RenEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11383)


Ischemic stroke occurs through a blockage of clogged blood vessels supplying blood to the brain. Segmentation of the stroke lesion is vital to improve diagnosis, outcome assessment and treatment planning. In this work, we propose a segmentation model with adversarial learning for ischemic lesion segmentation. We adopt U-Net with skip connection and dropout as segmentation baseline network and a fully connected network (FCN) as discriminator network. Discriminator network consists of 5 convolution layers followed by leaky-ReLU and an upsampling layer to rescale the output to the size of the input map. Training a segmentation network along with an adversarial network can detect and correct higher order inconsistencies between the segmentation maps produced by ground-truth and the Segmentor. We exploit three modalities (CT, DPWI, CBF) of acute computed tomography (CT) perfusion data provided in ISLES 2018 (Ischemic Stroke Lesion Segmentation) for ischemic lesion segmentation. Our model has achieved dice accuracy of 42.10% with the cross-validation of training and 39% with the testing data.



This work is supported by the Singapore Academic Research Fund under Grant R-397-000-227-112, NUSRI China Jiangsu Provincial Grant BK20150386 and BE2016077 and NMRC Bedside & Bench under grant R-397-000-245-511 awarded to Dr. Hongliang Ren.


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Mobarakol Islam
    • 1
    • 2
  • N. Rajiv Vaidyanathan
    • 2
    • 3
  • V. Jeya Maria Jose
    • 2
    • 4
  • Hongliang Ren
    • 2
    Email author
  1. 1.NUS Graduate School for Integrative Sciences and Engineering (NGS)National University of SingaporeSingaporeSingapore
  2. 2.Department of Biomedical EngineeringNational University of SingaporeSingaporeSingapore
  3. 3.Department of Mechanical EngineeringNITTiruchirappalliIndia
  4. 4.Department of Instrumentation and Control EngineeringNITTiruchirappalliIndia

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