https://github.com/bigmb/unet-segmentation-pytorch-nest-of-unets

Implementation of different kinds of Unet Models for Image Segmentation - Unet , RCNN-Unet, Attention Unet, RCNN-Attention Unet, Nested Unet
https://github.com/bigmb/unet-segmentation-pytorch-nest-of-unets

imagesegmentation python3 pytorch segmentation tensorvision torch torchvision unet

Last synced: 30 days ago
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Implementation of different kinds of Unet Models for Image Segmentation - Unet , RCNN-Unet, Attention Unet, RCNN-Attention Unet, Nested Unet

• Host: GitHub
• URL: https://github.com/bigmb/unet-segmentation-pytorch-nest-of-unets
• Owner: bigmb
• License: mit
• Created: 2019-05-14T15:45:56.000Z (over 5 years ago)
• Default Branch: master
• Last Pushed: 2022-11-28T17:44:40.000Z (almost 2 years ago)
• Last Synced: 2024-10-14T10:43:02.360Z (30 days ago)
• Topics: imagesegmentation, python3, pytorch, segmentation, tensorvision, torch, torchvision, unet
• Language: Python
• Homepage:
• Size: 605 KB
• Stars: 1,869
• Watchers: 16
• Forks: 345
• Open Issues: 3
• Metadata Files:
  • Readme: README.md
  • License: LICENSE
  • Code of conduct: CODE_OF_CONDUCT.md

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README

        
# Unet-Segmentation-Pytorch-Nest-of-Unets

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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)

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**

  ![unet1](/images/unet1.png)

  

  **RCNN Unet**

  ![r2unet](/images/r2unet.png)

  

  

  **Attention Unet**

  ![att-unet](/images/att-unet.png)

  

  

  **Attention-RCNN Unet**

  ![att-r2u](/images/att-r2u.png)

  

  

  **Nested Unet**

  

  ![nested](/images/nested.jpg)

## 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**

![l2](/images/l2.png)

**Filter Visulization**

![filt1](/images/filt1.png)

**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)



**Input Image Visulization for checking**

**a) Original Image**



**b) CenterCrop Image**



## 6. Results

**Dice Score for hippocampus segmentation**

ADNI-LONI Dataset



## 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

```