Welcome to the DSH-Net

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/DSH-Net
cd DSH-Net
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

Setting up the datasets

./DSH-Net/
./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 DSH-Net?

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