Welcome to the LP-Swin

News

[8/11/2025] LP-Swin, this code is directly related to our manuscript currently submitted to The Visual Computer. If you use this code or find it helpful in your research, please cite the following paper: [LP-Swin: Enhancing MRI Brain Tumor Segmentation through Laplacian Pyramid-Guided Swin Transformer], The Visual Computer, [2025].

BibTeX:

@article{Jiang2025,
  title   = {LP-Swin: Enhancing MRI Brain Tumor Segmentation through Laplacian Pyramid-Guided Swin Transformer},
  author  = {Jie Jiang},
  journal = {The Visual Computer},
  year    = {2025},
  doi     = {}
}

Installation

We recommend installation of the required packages using the conda package manager, available through the Anaconda Python distribution. Anaconda is available free of charge for non-commercial use through Anaconda Inc. After installing Anaconda and cloning this repository, For use as integrative framework：

git clone https://github.com/springbreeze7111/LP-Swin
cd LP-Swin
pip install -e.
cd dynamic-network-architectures
pip install -e.

Dataset download

Datasets can be acquired via following links:

Dataset I MSD

Dataset II BRATS2021

Dataset III ACDC

Setting up the datasets

./LP-Swin/
./DATASET/
  ├── nnUNet_raw/
       ├── nnUNet_raw_data/
           ├── Task01_BrainTumour/
  ├── nnUNet_trained_models/
  ├── nnUNet_preprocessed/

Setting your environment variables

export nnUNet_raw="/root/autodl-tmp/nnUNet_raw"
export nnUNet_preprocessed="/root/autodl-tmp/nnUNet_preprocessed"
export nnUNet_results="/root/autodl-tmp/nnUNet_results"

How does LP-Swin?

First we need to convert the MSD dataset format conversion to the format supported by the nnUNetv2 framework（You can refer to the usage of nnUNetv2）

nnUNetv2_convert_MSD_dataset -i \nnUNet\nnUNet_raw\Task01_BrainTumour

where -i is followed by the path to the dataset under your own nnUNet_raw_data/.

nnU-Net will extract a dataset fingerprint (a set of dataset-specific properties such as image sizes, voxel spacings, intensity information etc). This information is used to design three U-Net configurations. Each of these pipelines operates on its own preprocessed version of the dataset.

nnUNetv2_plan_and_preprocess -d DATASET_ID --verify_dataset_integrity

We can change the desired network (change the value of “network_class_name” to the used network) and some parameters (patch_size, batch_size, some network parameters, etc.) in nnUNetPlans.json in the generated nnUNet_preprocessed folder.

Training models is done with the nnUNetv2_train command. The general structure of the command is:

nnUNetv2_train DATASET_NAME_OR_ID UNET_CONFIGURATION FOLD [additional options, see -h]

UNET_CONFIGURATION is a string that identifies the requested U-Net configuration (defaults: 2d, 3d_fullres, 3d_lowres, 3d_cascade_lowres). DATASET_NAME_OR_ID specifies what dataset should be trained on and FOLD specifies which fold of the 5-fold-cross-validation is trained.

Acknowledgements

Code copied a lot from nnUnetv2

Cite

Literature citation will be added to the paper upon acceptance