Sparsely Connected Convolutional Layers in CNNs for Liver Segmentation in CT

  • Alena-Kathrin SchnurrEmail author
  • Lothar R. Schad
  • Frank G. Zöllner
Conference paper
Part of the Informatik aktuell book series (INFORMAT)


Convolutional neural networks are currently the best working solution for automatic liver segmentation. Generally, each convolutional layer processes all feature maps from the previous layer. We show that the introduction of sparsely connected convolutional layers into the U-Net architecture can benefit the quality of liver segmentation and results in the increase of the dice coeffcient by 0:32% and a reduction of the mean surface distance by 3:84 mm on the LiTS data. Evaluation on the IRCAD data set with the application of post-processing showed a 0:70% higher Dice coeffcient and a 0:26 mm lower mean surface distance.


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Copyright information

© Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature 2019

Authors and Affiliations

  • Alena-Kathrin Schnurr
    • 1
    Email author
  • Lothar R. Schad
    • 1
  • Frank G. Zöllner
    • 1
  1. 1.Computer Assisted Clinical Medicine, Medical Faculty MannheimHeidelberg UniversityHeidelbergDeutschland

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