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A PyTorch implementation for V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation
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Matt Macy
Latest commit a00c8ea Mar 25, 2017

README.md

A PyTorch implementation of V-Net

Vnet is a PyTorch implementation of the paper V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation by Fausto Milletari, Nassir Navab, and Seyed-Ahmad Ahmadi. Although this implementation is still a work in progress, I'm seeing a respectable 0.355% test error rate and a Dice coefficient of .9825 segmenting lungs from the LUNA16 data set after 249 epochs. The official implementation is available in the faustomilletari/VNet repo on GitHub.

This implementation relies on the LUNA16 loader and dice loss function from the Torchbiomed package.

Differences with the official version

This version uses batch normalization and dropout. Lung volumes in CTs are ~10% of the scan volume - a not too unreasonable class balance. For this particular test application I've added the option of using NLLoss instead of the Dice Coefficient.

What does the PyTorch compute graph of Vnet look like?

You can see the compute graph here, which I created with make_graph.py, which I copied from densenet.pytorch which in turn was copied from Adam Paszke's gist.

Credits

The train.py script was derived from the one in the densenet.pytorch repo.

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