Cycle-consistent adversarial network with polyphase U-Nets for liver lesion segmentation

Boah Kim, Jong Chul Ye

Apr 11, 2018 (modified: May 16, 2018) MIDL 2018 Abstract Submission readers: everyone
  • Abstract: We proposed a novel deep learning algorithm for liver lesion segmentation using a cycle-consistent generative adversarial network (cycleGAN) architecture. In order to overcome the mode collapsing phenomenon from many-to-one mapping nature of segmentation, our method discovers relationships between the computed tomography (CT) images and segmentation-augmented CT images through a cyclical constraint. Moreover, to retain the accurate boundary information, we employ an improved U-Net architecture called the polyphase U-Net as a generator, inspired by the recent theory of deep convolutional framelets. The performance improvement by the proposed method was evaluated on the Liver Tumor Segmentation Challenge 2017 datasets.
  • Author affiliation: Korea Advanced Institute of Science and Technology
  • Keywords: liver lesion segmentation, cycleGAN, polyphase U-Net
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