ABCnet

Adversarial Bias Correction Network for Infant Brain MR Images.

Architecture

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How to use it

Prerequisites

• Linux
• NVIDIA GPU with at least 8Gb memory
• CUDA CuDNN

Python Dependencies

• python (3.6)
• pytorch (0.4.1+)
• torchvision (0.2.1+)
• tensorboardx (1.6+)

Data preparation

Resample and convert the input files into npy format with size of 256x256x256.

Train

Modify the train.py file to match the training data in your own path. Then, run:

python train.py --dataroot /training_data_folder --name project_name --model pix2pix3d --direction AtoB --dataset_mode unaligned --input_nc 1 --output_nc 1 --gpu_ids 0

Test

python test.py --dataroot /data_folder --name project_name --model pix2pix3d --direction AtoB --dataset_mode unaligned --input_nc 1 --output_nc 1 --gpu_ids 0

Citations

If you use this code for your research, please cite as:

Chen, Liangjun, et al. "ABCnet: Adversarial bias correction network for infant brain MR images." Medical Image Analysis 72 (2021): 102133.