An optimized, robust and self-adapting framework for U-Net based medical image segmentation
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
The following datasets were used to train this model:
- MSD Task01 - Dataset with complex and heterogeneously-located targets.
Performance numbers for this model are available in NGC.