MT-Net

We provide Pytorch implementations for our paper Multi-scale Transformer Network for Cross-Modality MR Image Synthesis (IEEE TMI) by Yonghao Li, Tao Zhou, Kelei He, Yi Zhou, and Dinggang Shen.

1. Introduction

We propose MT-Net to leverage to take advantage of both paired and unpaired data in MRI synthesis.

Figure 2. An overview of the proposed MT-Net.

Preview:

Our proposed methods consist of two main components under two different settings:

• Edge-MAE (Self-supervised pre-training with image imputation and edge map estimation).

• MT-Net (Cross-modality MR image synthesis)

Note that our pre-trained Edge-MAE can be utilized for various downstream tasks, such as segmentation or classification.

Figure 2. Example of imputed image and estimated edge maps from the BraTS2020 dataset.

2. Getting Started

• Installation

  Install PyTorch and torchvision from http://pytorch.org and other dependencies. You can install all the dependencies by

  pip install -r requirements.txt

• Dataset Preparation

  Download BraTS2020 dataset from kaggle. The file name should be ./data/archive.zip. Unzip the file in ./data/.

• Date Preprocessing

  After preparing all the data, run the ./utils/preprocessing.py to normalize the data to [0,1] and crop out an image of size 200×200 from the center.

• Pre-training

  To pre-train our Edge-MAE, run pretrain.py. You may change the default settings in the ./options/pretrain_options.py. For instance, increase num_workers to speed up fine-tuning. The weights will be saved in ./weight/EdgeMAE/. You can also use the pre-trained checkpoints of Edge-MAE in the ./weight/EdgeMAE/.

• Fine-tuning

  To fine-tune our MT-Net, run Finetune.py. You may change the default settings in the ./options/finetune_options.py, especially the data_rate option to adjust the amount of paired data for fine-tuning. Besides, Besides, you can increase num_workers to speed up fine-tuning. The weights will be saved in ./weight/finetuned/. Note that for MT-Net, the input size must be 256×256.

• Test

  When fine-tuning is completed, the weights of Edge-MAE and MT-Net will be saved in ./weight/finetune/. You can change the default settings in the ./options/test_options.py. Then, run test.py, and the synthesized image will be saved in ./snapshot/test/, and can obtain the PSNR, SSIM, and NMSE values.

3. Mindspore

We also provide Mindspore implementations for our paper, which is a new open source deep learning training/inference framework.

• Installation

  Install mindspore from https://www.mindspore.cn/ and other dependencies. Unzip ./mindspore/mindcv.zip, an open-source toolbox for computer vision research.

• Pre-training

  After preparing all the data, run ./mindspore/pretrain.py to pre-train our EdgeMAE. You may change the default settings in ./mindspore/pretrain.py.

• Fine-tuning

  When pre-training is completed, run ./mindspore/finetune.py. You may change the default settings in ./mindspore/finetune.py.

• Test

  When fine-tuning is completed, run ./mindspore/test.py. You may change the default settings in ./mindspore/test.py.

4. Citation

@ARTICLE{10158035,
  author={Li, Yonghao and Zhou, Tao and He, Kelei and Zhou, Yi and Shen, Dinggang},
  journal={IEEE Transactions on Medical Imaging}, 
  title={Multi-scale Transformer Network with Edge-aware Pre-training for Cross-Modality MR Image Synthesis}, 
  year={2023},
  volume={},
  number={},
  pages={1-1},
  doi={10.1109/TMI.2023.3288001}}

5. References

• BraTs 2020: [HERE]

• MAE: [HERE]

• SwinUNet: [HERE]