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Implementation of different kinds of Unet Models for Image Segmentation

1. UNet - U-Net: Convolutional Networks for Biomedical Image Segmentationhttps://arxiv.org/abs/1505.04597

2. RCNN-UNet - Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net) for Medical Image Segmentationhttps://arxiv.org/abs/1802.06955

3. Attention Unet - Attention U-Net: Learning Where to Look for the Pancreashttps://arxiv.org/abs/1804.03999

4. RCNN-Attention Unet - Attention R2U-Net : Just integration of two recent advanced works (R2U-Net + Attention U-Net)

5. Nested UNet - UNet++: A Nested U-Net Architecture for Medical Image Segmentationhttps://arxiv.org/abs/1807.10165

With Layer Visualization

Clone the repo:

git clone https://github.com/bigmb/Unet-Segmentation-Pytorch-Nest-of-Unets.git

python>=3.6torch>=0.4.0torchvisiontorchsummarytensorboardxnatsortnumpypillowscipyscikit-imagesklearn

Install all dependent libraries:

pip install -r requirements.txt

Add all your folders to this line 106-113

t_data = '' # Input datal_data = '' #Input Labeltest_image = '' #Image to be predicted while trainingtest_label = '' #Label of the prediction Imagetest_folderP = '' #Test folder Imagetest_folderL = '' #Test folder Label for calculating the Dice score

Unet

RCNN Unet

Attention Unet

Attention-RCNN Unet

Nested Unet

To plot the loss , Visdom would be required. The code is already written, just uncomment the required part.Gradient flow can be used too. Taken from (https://discuss.pytorch.org/t/check-gradient-flow-in-network/15063/10)

A model folder is created and all the data is stored inside that.Last layer will be saved in the model folder. If any particular layer is required , mention it in the line 361.

Layer Visulization

Filter Visulization

TensorboardXStill have to tweak some parameters to get visualization. Have messed up this trying to make pytorch 1.1.0 working with tensorboard directly (and then came to know Currently it doesn't support anything apart from linear graphs)

Input Image Visulization for checking

a) Original Image

b) CenterCrop Image

Dice Score for hippocampus segmentationADNI-LONI Dataset

If you find it usefull for your work.

@article{DBLP:journals/corr/abs-1906-07160,  author    = {Malav Bateriwala and               Pierrick Bourgeat},  title     = {Enforcing temporal consistency in Deep Learning segmentation of brain               {MR} images},  journal   = {CoRR},  volume    = {abs/1906.07160},  year      = {2019},  url       = {http://arxiv.org/abs/1906.07160},  archivePrefix = {arXiv},  eprint    = {1906.07160},  timestamp = {Mon, 24 Jun 2019 17:28:45 +0200},  biburl    = {https://dblp.org/rec/bib/journals/corr/abs-1906-07160},  bibsource = {dblp computer science bibliography, https://dblp.org}}

In progress