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

Clone the repo:

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

python>=3.6 torch>=0.4.0 torchvision torchsummary tensorboardx natsort numpy pillow scipy scikit-image sklearn 

Install all dependent libraries:

pip install -r requirements.txt

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

Input Image Visulization for checking

a) Original Image

b) CenterCrop Image

Dice Score for hippocampus segmentation