Model Overview

An optimized, robust and self-adapting framework for U-Net based medical image segmentation

Model Architecture

The nnU-Net allows training two types of networks: 2D U-Net and 3D U-Net to perform semantic segmentation of 2D or 3D images, with high accuracy and performance.

The following figure shows the architecture of the 3D U-Net model and its different components. U-Net is composed of a contractive and an expanding path, that aims at building a bottleneck in its centermost part through a combination of convolution, instance norm, and leaky ReLU operations. After this bottleneck, the image is reconstructed through a combination of convolutions and upsampling. Skip connections are added with the goal of helping the backward flow of gradients to improve the training.

Figure 1: The 3D U-Net architecture

Training

This model was trained using script available on NGC and in GitHub repo.

Dataset

The following datasets were used to train this model:

• MSD Task01 - Dataset with complex and heterogeneously-located targets.

Performance

Performance numbers for this model are available in NGC.

References

• Original paper
• NVIDIA model implementation in NGC
• NVIDIA model implementation on GitHub

License

This model was trained using open-source software available in Deep Learning Examples repository. For terms of use, please refer to the license of the script and the datasets the model was derived from.