V-Net Medical TensorFlow checkpoint (FP32)

Model Overview

V-Net is a convolutional neural network for 3D image segmentation.

Model Architecture

V-Net was first introduced by Fausto Milletari, Nassir Navab, Seyed-Ahmad Ahmadi in the paper: V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation. V-Net allows for seamless segmentation of 3D images, with high accuracy and performance, and can be adapted to solve many different segmentation problems.

The following figure shows the construction of the standard V-Net model and its different components. V-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 and downsampling. 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 in order to improve the training.

Figure 1. VNet 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 Task04 - Dataset for segmenting two neighbouring small structures with high precision.

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.