UNet++ is a new general purpose image segmentation architecture for more accurate image segmentation. UNet++ consists of U-Nets of varying depths whose decoders are densely connected at the same resolution via the redesigned skip pathways, which aim to address two key challenges of the U-Net: 1) unknown depth of the optimal architecture and 2) the unnecessarily restrictive design of skip connections.

This repository provides the official Keras implementation of UNet++ in 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

• keras/
• pytorch/

• [PyTorch] (by Pavel Yakubovskiy)
• [PyTorch] (by 4ui_iurz1)
• [PyTorch] (by Hong Jing)
• [PyTorch] (by ZJUGiveLab)
• [PyTorch] (by MontaEllis)
• [Keras] (by Siddhartha)

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},
  year={2019},
  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}
}

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.