基于深度学习扩散模型的医学图像中的无监督异常检测内含数据集和环境搭建说明.zip

# patched-Diffusion-Models-UAD Codebase for the paper [Patched Diffusion Models for Unsupervised Anomaly Detection](https://arxiv.org/abs/2303.03758) accepted at MIDL23. ## Graphical abstract  ## Data We use the IXI data set, the BraTS21 data set and the MSLUB data set for our experiments. You can download/request the data sets here: * IXI: https://brain-development.org/ixi-dataset/ * BraTS21: http://braintumorsegmentation.org/ * MSLUB: https://lit.fe.uni-lj.si/en/research/resources/3D-MR-MS/ ## Data Preprocessing # Warning: The Preprocessing is bugged right now and does not represent the steps taken in the paper. A fix will be pushed asap. Before processing, you need to extract the downloaded zip files and organize them as follows: ├── IXI │ ├── t2 │ │ ├── IXI1.nii.gz │ │ ├── IXI2.nii.gz │ │ └── ... │ └── ... ├── MSLUB │ ├── t2 │ │ ├── MSLUB1.nii.gz │ │ ├── MSLUB2.nii.gz │ │ └── ... │ ├── seg │ │ ├── MSLUB1_seg.nii.gz │ │ ├── MSLUB2_seg.nii.gz │ │ └── ... │ └── ... ├── Brats21 │ ├── t2 │ │ ├── Brats1.nii.gz │ │ ├── Brats2.nii.gz │ │ └── ... │ ├── seg │ │ ├── Brats1_seg.nii.gz │ │ ├── Brats2_seg.nii.gz │ │ └── ... │ └── ... └── ... We apply several preprocessing steps to the data, including resampling to 1.0 mm, skull-stripping with HD-BET, registration to the SRI Atlas, cutting black boarders and N4 Bias correction. To run the preprocessing, you need to clone and setup the [HD-BET](https://github.com/MIC-DKFZ/HD-BET) tool for skull-stripping. For each data set there is an individual bash script that performs the preprocessing in the [preprocessing](preprocessing) directory. To preprocess the data, go to the [preprocessing](preprocessing) directory: cd preprocessing execute the bash script: bash prepare_IXI.sh <input_dir> <output_dir> the <input_dir> refers to the directory where the downloaded, raw data is stored. Note, that you need to provide absolute paths and this script will use a GPU for skull-stripping. Example for the IXI data set: bash prepare_IXI.sh /raw_data/IXI/ $(pwd) This will create 4 different folders with the results of the intermediate preprocessing steps. The final scans are located in /processed_data/v4correctedN4_non_iso_cut After preprocessing, place the data (the folder v4correctedN4_non_iso_cut) in your DATA_DIR. cp -r <output_dir>/IXI <DATA_DIR>/Train/ixi cp -r <output_dir>/MSLUB <DATA_DIR>/Test/MSLUB cp -r <output_dir>/Brats21 <DATA_DIR>/Test/Brats21 The directory structure of <DATA_DIR> should look like this: <DATA_DIR> ├── Train │ ├── ixi │ │ ├── mask │ │ ├── t2 ├── Test │ ├── Brats21 │ │ ├── mask │ │ ├── t2 │ │ ├── seg │ ├── MSLUB │ │ ├── mask │ │ ├── t2 │ │ ├── seg ├── splits │ ├── Brats21_test.csv │ ├── Brats21_val.csv │ ├── MSLUB_val.csv │ ├── MSLUB_test.csv │ ├── IXI_train_fold0.csv │ ├── IXI_train_fold1.csv │ └── ... └── ... You should then specify the location of <DATA_DIR> in the pc_environment.env file. Additionally, specify the <LOG_DIR>, where runs will be saved. ## Environment Set-up To download the code type git clone git@github.com:FinnBehrendt/patched-Diffusion-Models-UAD.git In your linux terminal and switch directories via cd patched-Diffusion-Models-UAD To setup the environment with all required packages and libraries, you need to install anaconda first. Then, run conda env create -f environment.yml -n pddpm-uad and subsequently run conda activate pddpm-uad pip install -r requirements.txt to install all required packages. ## Run Experiments To run the training and evaluation of the pDDPM, simply execute python run.py experiment=MIDL23_DDPM/DDPM_patched in your terminal. ## Citation If you make use of our work, we would be happy if you cite it via @article{behrendt2023patched, title={Patched diffusion models for unsupervised anomaly detection in brain mri}, author={Behrendt, Finn and Bhattacharya, Debayan and Kr{\"u}ger, Julia and Opfer, Roland and Schlaefer, Alexander}, journal={arXiv preprint arXiv:2303.03758}, year={2023} }