yaoppeng / U-Net_v2

Pytorch implementation of U-Net v2: RETHINKING THE SKIP CONNECTIONS OF U-NET FOR MEDICAL IMAGE SEGMENTATION

nnUNet is the GOAT! Thanks to Fabian et al. for making pure U-Net great again. Less is more.

Please make sure you have installed all the packages with the correct versions as shown in requirements.txt. Most of the issues are caused by incompatible package versions.

The pretrained PVT model: google drive

1 ISIC segmentation

Download the dataset from google drive

set the nnUNet_raw, nnUNet_preprocessed and nnUNet_results environment variable using the following command:

export nnUNet_raw=/path/to/input_raw_dir
export nnUNet_preprocessed=/path/to/preprocessed_dir
export nnUNet_results=/path/to/result_save_dir

run the training and testing using the following command:

python /path/to/U-Net_v2/run/run_training.py dataset_id 2d 0 --no-debug -tr ISICTrainer --c

The nnUNet preprocessed data can be downloaded from ISIC 2017 and ISIC 2018

2. Polyp segmentation

Download the training dataset from google drive and testing dataset from google drive

run the training and testing using the following command:

python /path/to/U-Net_v2/PolypSeg/Train.py

3. On your own data

I only used the 4× downsampled results on my dataset. You may need to modify the code:

f1, f2, f3, f4, f5, f6 = self.encoder(x)

...
f61 = self.sdi_6([f1, f2, f3, f4, f5, f6], f6)
f51 = self.sdi_5([f1, f2, f3, f4, f5, f6], f5)
f41 = self.sdi_4([f1, f2, f3, f4, f5, f6], f4)
f31 = self.sdi_3([f1, f2, f3, f4, f5, f6], f3)
f21 = self.sdi_2([f1, f2, f3, f4, f5, f6], f2)
f11 = self.sdi_1([f1, f2, f3, f4, f5, f6], f1)

and delete the following code:

for i, o in enumerate(seg_outs):
    seg_outs[i] = F.interpolate(o, scale_factor=4, mode='bilinear')

By doing this, you are using all the resolution results rather than the 4× downsampled ones.

The following code snippet shows how to use U-Net v2 in training and testing.

For training:

from unet_v2.UNet_v2 import *

n_classes=2
pretrained_path="/path/to/pretrained/pvt"
model = UNetV2(n_classes=n_classes, deep_supervision=True, pretrained_path=pretrained_path)

x = torch.rand((2, 3, 256, 256))

ys = model(x)  # ys is a list because of deep supervision

Now you can use ys and label to compute the loss and do back-propagation.

In the testing phase:

model.eval()
model.deep_supervision = False

x = torch.rand((2, 3, 256, 256))
y = model(x)  # y is a tensor since the deep supervision is turned off in the testing phase
print(y.shape)  # (2, n_classes, 256, 256)

pred = torch.argmax(y, dim=1)

for convience, the U-Net v2 model file has been copied to ./unet_v2/UNet_v2.py

4. Citation

@article{peng2023u,
  title={U-Net v2: Rethinking the Skip Connections of U-Net for Medical Image Segmentation},
  author={Peng, Yaopeng and Sonka, Milan and Chen, Danny Z},
  journal={arXiv preprint arXiv:2311.17791},
  year={2023}
}