README.md

nnUNet YYDS

Although has been published in Nature Methods for a few years, nnUNet still stands out as a strong segmentation framework. We also build a VoCo trainer for nnUNet implementation. However, it is worth noting that our models are pre-trained with monai and transfer to nnUNet. Thus, the pre-processing settings are not consistent between pre-training and finetuing, which may hinder its performance. We will investigate how to pre-train with nnUNet in the future.

Usage

For usage, you need to follow the clear instructions in nnUNet. Here, we take Dataset503_VoComni as an example to show the implementation. This dataset can be used for fully-supervised pre-training nnUNet.

Pre-trained Models

Model	Params	Checkpoint
VoComni_nnunet	31M	Download

You need to modify the path of pre-trained models in nnUNetTrainer_pretrain.py.

Download Datasets

Download the datasets from our Hugging face. Take VoComni as an example:

├── /nnunet_data/nnUNet_raw/Dataset503_VoComni
    ├── imagesTr
        ├── VoComni_0_0000.nii.gz
        ├──...
    ├── labelsTr
        ├── VoComni_0.nii.gz
        ├──...
    └── dataset.json

Fine-tuning

cd nnUNet
source activate YOUR-CONDA-ENVIRONMENT
nnUNetv2_plan_and_preprocess -d 503 -c 3d_fullres --verbose --verify_dataset_integrity
nnUNetv2_train 503 3d_fullres all -tr nnUNetTrainer_pre

Citation

If you find this repo useful for your research, please consider citing the paper as follows:

@InProceedings{voco-v1,
    author    = {Wu, Linshan and Zhuang, Jiaxin and Chen, Hao},
    title     = {VoCo: A Simple-yet-Effective Volume Contrastive Learning Framework for 3D Medical Image Analysis},
    booktitle = {CVPR},
    month     = {June},
    year      = {2024},
    pages     = {22873-22882}
}
@article{nnunet,
  title={nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation},
  author={Isensee, Fabian and others},
  journal={Nature Methods},
  volume={18},
  number={2},
  pages={203--211},
  year={2021},
}