V-Net is a convolutional neural network for 3D image segmentation.
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
This model was trained using script available on NGC and in GitHub repo
The following datasets were used to train this model:
Performance numbers for this model are available in NGC