不同类型的图像分割Unet模型的实现

# Unet-Segmentation-Pytorch-Nest-of-Unets [](https://www.python.org/) [](http://hits.dwyl.io/bigmb/Unet-Segmentation-Pytorch-Nest-of-Unets) [](https://opensource.org/licenses/MIT) [](https://github.com/bigmb/Unet-Segmentation-Pytorch-Nest-of-Unets/graphs/commit-activity) [](https://github.com/bigmb/Unet-Segmentation-Pytorch-Nest-of-Unets/issues) [](https://paperswithcode.com/sota/semantic-segmentation-on-cityscapes-val?p=unet-a-nested-u-net-architecture-for-medical) Implementation of different kinds of Unet Models for Image Segmentation 1) **UNet** - U-Net: Convolutional Networks for Biomedical Image Segmentation https://arxiv.org/abs/1505.04597 2) **RCNN-UNet** - Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net) for Medical Image Segmentation https://arxiv.org/abs/1802.06955 3) **Attention Unet** - Attention U-Net: Learning Where to Look for the Pancreas https://arxiv.org/abs/1804.03999 4) **RCNN-Attention Unet** - Attention R2U-Net : Just integration of two recent advanced works (R2U-Net + Attention U-Net) <!--LeeJun Implementation - https://github.com/LeeJunHyun/Image_Segmentation.git --> 5) **Nested UNet** - UNet++: A Nested U-Net Architecture for Medical Image Segmentation https://arxiv.org/abs/1807.10165 With Layer Visualization ## 1. Getting Started Clone the repo: ```bash git clone https://github.com/bigmb/Unet-Segmentation-Pytorch-Nest-of-Unets.git ``` ## 2. Requirements ``` python>=3.6 torch>=0.4.0 torchvision torchsummary tensorboardx natsort numpy pillow scipy scikit-image sklearn ``` Install all dependent libraries: ```bash pip install -r requirements.txt ``` ## 3. Run the file Add all your folders to this line 106-113 ``` t_data = '' # Input data l_data = '' #Input Label test_image = '' #Image to be predicted while training test_label = '' #Label of the prediction Image test_folderP = '' #Test folder Image test_folderL = '' #Test folder Label for calculating the Dice score ``` ## 4. Types of Unet **Unet**  **RCNN Unet**  **Attention Unet**  **Attention-RCNN Unet**  **Nested Unet**  ## 5. Visualization 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**  **TensorboardX** Still 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) <img src="https://github.com/bigmb/Unet-Segmentation-Pytorch-Nest-of-Unets/blob/master/images/tensorb.png" width="280"> **Input Image Visulization for checking** **a) Original Image** <img src="https://github.com/bigmb/Unet-Segmentation-Pytorch-Nest-of-Unets/blob/master/images/in1.png" width="480"> **b) CenterCrop Image** <img src="https://github.com/bigmb/Unet-Segmentation-Pytorch-Nest-of-Unets/blob/master/images/in2.png" width="480"> ## 6. Results **Dice Score for hippocampus segmentation** ADNI-LONI Dataset <img src="https://github.com/bigmb/Unet-Segmentation-Pytorch-Nest-of-Unets/blob/master/dice.png" width="380"> ## 7. Citation 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} } ``` ## 8. Blog about different Unets ``` In progress ```