PyTorch implementation of UNet++ (Nested U-Net)

This repository contains code for a image segmentation model based on UNet++: A Nested U-Net Architecture for Medical Image Segmentation implemented in PyTorch.

Requirements

• PyTorch 1.x or 0.41

Installation

1. Create an anaconda environment.

conda create -n=<env_name> python=3.6 anaconda
conda activate <env_name>

2. Install PyTorch.

conda install pytorch torchvision cudatoolkit=10.1 -c pytorch

3. Install pip packages.

pip install -r requirements.txt

Training on HVSMR 2016 dataset

Make sure to put the files as the following structure (e.g. the number of classes is 2):

inputs
└── <dataset name>
    ├── images
    |   ├── 0a7e06.jpg
    │   ├── 0aab0a.jpg
    │   ├── 0b1761.jpg
    │   ├── ...
    |
    └── masks
        ├── 0
        |   ├── 0a7e06.png
        |   ├── 0aab0a.png
        |   ├── 0b1761.png
        |   ├── ...
        |
        └── 1
            ├── 0a7e06.png
            ├── 0aab0a.png
            ├── 0b1761.png
            ├── ...

1. Train the model.

python train.py --dataset <dataset name> --arch NestedUNet --img_ext .jpg --mask_ext .png

2. Evaluate.

python val.py --name <dataset name>_NestedUNet_woDS