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ISLES Challenge: U-Shaped Convolution Neural Network with Dilated Convolution for 3D Stroke Lesion Segmentation

  • Alzbeta TureckovaEmail author
  • Antonio J. Rodríguez-Sánchez
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11383)

Abstract

In this paper, we propose the algorithm for stroke lesion segmentation based on a deep convolutional neural network (CNN). The model is based on U-shaped CNN, which has been applied successfully to other medical image segmentation tasks. The network architecture was derived from the model presented in Isensee et al. [1] and is capable of processing whole 3D images. The model incorporates the convolution layers through upsampled filters – also known as dilated convolution. This change enlarges filter’s field of the view and allows the net to integrate larger context into the computation. We add the dilated convolution into different parts of network architecture and study the impact on the overall model performance. The best model which uses the dilated convolution in the input of the net outperforms the original architecture in nearly all used evaluation metrics. The code and trained models can be found on the GitHub website: http://github.com/tureckova/ISLES2018/.

Keywords

Medical image segmentation Deep convolutional neural networks U-Net Dilated convolution 

Notes

Acknowledgments

This work was supported by the Internal Grant Agency of Tomas Bata University under the Project no. IGA/CebiaTech/2018/003 and further by resources of A. I. Lab (https://ailab.fai.utb.cz/) and IIS group at the University of Innsbruck (https://iis.uibk.ac.at/).

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Alzbeta Tureckova
    • 1
    Email author
  • Antonio J. Rodríguez-Sánchez
    • 2
  1. 1.Faculty of Applied InformaticsTomas Bata University in ZlinZlinCzech Republic
  2. 2.Intelligent and Interactive Systems, Institute of Computer ScienceUniversity of InnsbruckInnsbruckAustria

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