UNetPlusPlus

UNet++ is a nested U-Net architecture for medical image segmentation. It consists of U-Nets of varying depths whose decoders are densely connected at the same resolution through redesigned skip pathways. This addresses two limitations of the original U-Net: the unknown optimal depth of the architecture, and the unnecessarily restrictive design of its skip connections.

Paper

This repository provides the official Keras and PyTorch implementations of UNet++, from the following papers:

UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation Zongwei Zhou, Md Mahfuzur Rahman Siddiquee, Nima Tajbakhsh, and Jianming Liang Arizona State University IEEE Transactions on Medical Imaging (TMI) paper | code

UNet++: A Nested U-Net Architecture for Medical Image Segmentation Zongwei Zhou, Md Mahfuzur Rahman Siddiquee, Nima Tajbakhsh, and Jianming Liang Arizona State University Deep Learning in Medical Image Analysis (DLMIA) 2018. (Oral) paper | code | slides | poster | blog

Official implementation

• keras/
• pytorch/

Other implementation

• [PyTorch] (by Pavel Yakubovskiy)
• [PyTorch] (by 4ui_iurz1)
• [PyTorch] (by ZJUGiveLab)
• [PyTorch] (by MontaEllis, deprecated)
• [Keras] (by Siddhartha)

Citation

If you use UNet++ for your research, please cite our papers:

@article{zhou2019unetplusplus,
  title={UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation},
  author={Zhou, Zongwei and Siddiquee, Md Mahfuzur Rahman and Tajbakhsh, Nima and Liang, Jianming},
  journal={IEEE Transactions on Medical Imaging},
  volume={39},
  number={6},
  pages={1856--1867},
  year={2020},
  doi={10.1109/TMI.2019.2959609},
  publisher={IEEE}
}

@incollection{zhou2018unetplusplus,
  title={UNet++: A Nested U-Net Architecture for Medical Image Segmentation},
  author={Zhou, Zongwei and Siddiquee, Md Mahfuzur Rahman and Tajbakhsh, Nima and Liang, Jianming},
  booktitle={Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support},
  pages={3--11},
  year={2018},
  publisher={Springer}
}

@phdthesis{zhou2021towards,
  title={Towards Annotation-Efficient Deep Learning for Computer-Aided Diagnosis},
  author={Zhou, Zongwei},
  year={2021},
  school={Arizona State University}
}

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. This is a patent-pending technology.