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UNet++: A Nested U-Net Architecture for Medical Image Segmentation

  • Zongwei Zhou
  • Md Mahfuzur Rahman Siddiquee
  • Nima Tajbakhsh
  • Jianming LiangEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11045)

Abstract

In this paper, we present UNet++, a new, more powerful architecture for medical image segmentation. Our architecture is essentially a deeply-supervised encoder-decoder network where the encoder and decoder sub-networks are connected through a series of nested, dense skip pathways. The re-designed skip pathways aim at reducing the semantic gap between the feature maps of the encoder and decoder sub-networks. We argue that the optimizer would deal with an easier learning task when the feature maps from the decoder and encoder networks are semantically similar. We have evaluated UNet++ in comparison with U-Net and wide U-Net architectures across multiple medical image segmentation tasks: nodule segmentation in the low-dose CT scans of chest, nuclei segmentation in the microscopy images, liver segmentation in abdominal CT scans, and polyp segmentation in colonoscopy videos. Our experiments demonstrate that UNet++ with deep supervision achieves an average IoU gain of 3.9 and 3.4 points over U-Net and wide U-Net, respectively.

Notes

Acknowledgments

This research has been supported partially by NIH under Award Number R01HL128785, by ASU and Mayo Clinic through a Seed Grant and an Innovation Grant. The content is solely the responsibility of the authors and does not necessarily represent the official views of NIH.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Zongwei Zhou
    • 1
  • Md Mahfuzur Rahman Siddiquee
    • 1
  • Nima Tajbakhsh
    • 1
  • Jianming Liang
    • 1
    Email author
  1. 1.Arizona State UniversityTempeUSA

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