nnFormer: Volumetric Medical Image Segmentation via a 3D Transformer

At 2022/02/11, we rebuilt the code of nnFormer to match the performance reported in the latest draft. The results produced by new codes are more stable and thus easier to reproduce!

At 2022/02/26, we add seed in the file nnformer/run_training.py and set torch.backends.cudnn.benchmark and torch.backends.cudnn.enabled as true to improve efficiency.

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Installation

1. System requirements

We run nnFormer on a system running Ubuntu 18.01, with Python 3.6, PyTorch 1.8.1, and CUDA 10.1. For a full list of software packages and version numbers, see the Conda environment file environment.yml.

This software leverages graphical processing units (GPUs) to accelerate neural network training and evaluation. Thus, systems lacking a suitable GPU would likely take an extremely long time to train or evaluate models. The software was tested with the NVIDIA RTX 2080 TI GPU, though we anticipate that other GPUs will also work, provided that the unit offers sufficient memory.

2. Installation guide

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/282857341/nnFormer.git
cd nnFormer
conda env create -f environment.yml
source activate nnFormer
pip install -e .

3. Functions of scripts and folders

• For evaluation:

  • nnFormer/nnformer/inference_acdc.py

  • nnFormer/nnformer/inference_synapse.py

  • nnFormer/nnformer/inference_tumor.py

• Data split:

  • nnFormer/nnformer/dataset_json/

• For inference:

  • nnFormer/nnformer/inference/predict_simple.py

• Network architecture:

  • nnFormer/nnformer/network_architecture/nnFormer_acdc.py

  • nnFormer/nnformer/network_architecture/nnFormer_synapse.py.py

  • nnFormer/nnformer/network_architecture/nnFormer_tumor.py.py

• For training:

  • nnFormer/nnformer/run/run_training.py

• Trainer for dataset:

  • nnFormer/nnformer/training/network_training/nnFormerTrainerV2_nnformer_acdc.py

  • nnFormer/nnformer/training/network_training/nnFormerTrainerV2_nnformer_synapse.py.py

  • nnFormer/nnformer/training/network_training/nnFormerTrainerV2_nnformer_tumor.py.py

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Training

1. Dataset download

Datasets can be acquired via following links:

Dataset I ACDC

Dataset II The Synapse multi-organ CT dataset

Dataset III Brain_tumor

The splits of all three datasets are available in nnFormer/nnformer/dataset_json/.

2. Setting up the datasets

After you have downloaded the datasets, you can follow the settings in nnUNet for path configurations and preprocessing procedures. Finally, your folders should be organized as follows:

./Pretrained_weight/
./nnFormer/
./DATASET/
  ├── nnFormer_raw/
      ├── nnFormer_raw_data/
          ├── Task01_ACDC/
              ├── imagesTr/
              ├── imagesTs/
              ├── labelsTr/
              ├── labelsTs/
              ├── dataset.json
          ├── Task02_Synapse/
              ├── imagesTr/
              ├── imagesTs/
              ├── labelsTr/
              ├── labelsTs/
              ├── dataset.json
          ├── Task03_tumor/
              ├── imagesTr/
              ├── imagesTs/
              ├── labelsTr/
              ├── labelsTs/
              ├── dataset.json
      ├── nnFormer_cropped_data/
  ├── nnFormer_trained_models/
  ├── nnFormer_preprocessed/

You can refer to nnFormer/nnformer/dataset_json/ for data split.

After that, you can preprocess the above data using following commands:

nnFormer_convert_decathlon_task -i ../DATASET/nnFormer_raw/nnFormer_raw_data/Task01_ACDC
nnFormer_convert_decathlon_task -i ../DATASET/nnFormer_raw/nnFormer_raw_data/Task02_Synapse
nnFormer_convert_decathlon_task -i ../DATASET/nnFormer_raw/nnFormer_raw_data/Task03_tumor

nnFormer_plan_and_preprocess -t 1
nnFormer_plan_and_preprocess -t 2
nnFormer_plan_and_preprocess -t 3

3. Training and Testing

• Commands for training and testing:

bash train_inference.sh -c 0 -n nnformer_acdc -t 1 
#-c stands for the index of your cuda device
#-n denotes the suffix of the trainer located at nnFormer/nnformer/training/network_training/
#-t denotes the task index

If you want use your own data, please create a new trainer file in the path nnformer/training/network_training and make sure the class name in the trainer file is the same as the trainer file. Some hyperparameters could be adjust in the trainer file, but the batch size and crop size should be adjust in the filennformer/run/default_configuration.py.

• You can download our pretrained model weights via this link. Then, you can put model weights and their associated files in corresponding directories. For instance, on ACDC dataset, they should be like this:

../DATASET/nnFormer_trained_models/nnFormer/3d_fullres/Task001_ACDC/nnFormerTrainerV2_nnformer_acdc__nnFormerPlansv2.1/fold_0/model_best.model
../DATASET/nnFormer_trained_models/nnFormer/3d_fullres/Task001_ACDC/nnFormerTrainerV2_nnformer_acdc__nnFormerPlansv2.1/fold_0/model_best.model.pkl

4. Visualization Results

You can download the visualization results of nnFormer, nnUNet and UNETR from this link.

5. One Frequently Asked Problem

input feature has wrong size

If you encounter this problem during your implementation, please check the code in nnFormer/nnformer/run/default_configuration.py. I have set independent crop size (i.e., patch size) for each dataset. You may need to modify the crop size based on your own need.