A PyTorch implementation for V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation

Related tags

Deep Learning pytorch convolutional-neural-networks semantic-segmentation fully-convolutional-networks lung-segmentation
Overview

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.

Comments
  • Preprocess for LUNA16 dataset?

    Preprocess for LUNA16 dataset?

    Hi, I'm a quite a newer to LUNA16 dataset,and I seems your code need some preprocess for LANA16 dataset, I can see you list some functions in lana16.py,but I have no idea how to use it(e.g.fisrt to do so?and next what?),could you give me some simple process about it? Thanks a lot!

    opened by lilhope 3
  • no gpu run

    no gpu run

    hi , I was wondering if the code can be run without a gpu?

    build vnet Traceback (most recent call last): File "train.py", line 384, in main() File "train.py", line 145, in main model = nn.parallel.DataParallel(model, device_ids=gpu_ids) File "/home/andrewcz/miniconda3/lib/python3.5/site-packages/torch/nn/parallel/data_parallel.py", line 58, in init self.module.cuda(device_ids[0]) File "/home/andrewcz/miniconda3/lib/python3.5/site-packages/torch/nn/modules/module.py", line 143, in cuda return self._apply(lambda t: t.cuda(device_id)) File "/home/andrewcz/miniconda3/lib/python3.5/site-packages/torch/nn/modules/module.py", line 114, in _apply module._apply(fn) File "/home/andrewcz/miniconda3/lib/python3.5/site-packages/torch/nn/modules/module.py", line 114, in _apply module._apply(fn) File "/home/andrewcz/miniconda3/lib/python3.5/site-packages/torch/nn/modules/module.py", line 120, in _apply param.data = fn(param.data) File "/home/andrewcz/miniconda3/lib/python3.5/site-packages/torch/nn/modules/module.py", line 143, in return self._apply(lambda t: t.cuda(device_id)) File "/home/andrewcz/miniconda3/lib/python3.5/site-packages/torch/_utils.py", line 56, in _cuda with torch.cuda.device(device): File "/home/andrewcz/miniconda3/lib/python3.5/site-packages/torch/cuda/init.py", line 136, in enter _lazy_init() File "/home/andrewcz/miniconda3/lib/python3.5/site-packages/torch/cuda/init.py", line 96, in _lazy_init _check_driver() File "/home/andrewcz/miniconda3/lib/python3.5/site-packages/torch/cuda/init.py", line 70, in _check_driver http://www.nvidia.com/Download/index.aspx""") AssertionError: Found no NVIDIA driver on your system. Please check that you have an NVIDIA GPU and installed a driver

    Can it just be used with cpu. Many thanks, Best, Andrew

    opened by AIAdventures 3
  • Concatenation operation in InputTransition

    Concatenation operation in InputTransition

    If my understanding is right, the concatenation in the InputTransition block should be applied along dim=1 instead of dim=0, because the second dimension is channel. i.e.

    # split input in to 16 channels
x16 = torch.cat((x, x, x, x, x, x, x, x,
                 x, x, x, x, x, x, x, x), 0)

    should be

    # split input in to 16 channels
x16 = torch.cat((x, x, x, x, x, x, x, x,
                 x, x, x, x, x, x, x, x), 1)
    opened by RongzhaoZhang 3
  • dataloader for LUNA16

    dataloader for LUNA16

    Hi, Thanks for sharing i see that you have implemented a dataloader for dealing with the LUNA16 dataset, but I did not found it the the repo. Can you please guide me where I can find it.

    Thanks Saeed

    opened by saeedizadi 1
  • About the lunge segmentation mask file of your training ?

    About the lunge segmentation mask file of your training ?

    hi, my dear friend~ So nice to meet you! My problem is that in LUNA2016 there is no lunge segmentation mask file for training, how do you get that? segment by yourself or some other ways? Thank you!!!

    opened by l35633 1
  • OutputTransition

    OutputTransition

    After the final upTransition, input is 32 channels and should be processed through the 111 filter directly according to the image. but you first process the input through the kernel(555) and then kernel(111).So I can't understand why you do the extra step(kernel size 555), maybe I am wrong, please reply for me ,if you are free, thx

    opened by LiB98 0
  • Questions about implementation still actual ?

    Questions about implementation still actual ?

    Awesome and huge work, thank you a lot. I understand that original paper author confused you with network structure diagram (me too actually), but your implementation also have your own addons like dropouts and batch normalizations. Is it ok for me to ask a question about one thing in you code or you are already out of the game?))

    opened by andreys42 0
  • Question about up-convolution block

    Question about up-convolution block

    Here in line 106: https://github.com/mattmacy/vnet.pytorch/blob/a00c8ea16bcaea2bddf73b2bf506796f70077687/vnet.py#L106

    Should it be xcat = self.ops(xcat) instead? Thank you in advance!

    opened by xzhhzx 2
Owner
Matthew Macy
Matthew Macy
GitHub https://mattmacy.github.io/vnet.pytorch
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