Awesome

<hr> <h1 align="center"> I2I-Mamba Multi-modal medical image synthesis via selective state space modeling </h1> <div align="center"> <a href="https://github.com/OmerFarukAtli" target="_blank">Omer F. Atli</a>1,2 &ensp; · &ensp; <a href="https://bilalkabas.github.io/" target="_blank">Bilal Kabas</a>1,2 &ensp; · &ensp; <a href="https://github.com/fuat-arslan" target="_blank">Fuat Arslan</a>1,2 &ensp; · &ensp; <a href="https://scholar.google.com.tr/citations?user=oAXHlRMAAAAJ&hl=en" target="_blank">Mahmut Yurt</a>3 &ensp; · &ensp; <a href="https://onatdalmaz.com/" target="_blank">Onat Dalmaz</a>3 &ensp; · &ensp; <a href="https://kilyos.ee.bilkent.edu.tr/~cukur/" target="_blank">Tolga Çukur</a>1,2 &ensp;

1Bilkent University 2UMRAM 3Stanford University

</div> <hr> <h3 align="center">[<a href="https://arxiv.org/abs/2405.14022">arXiv</a>]</h3>

Official PyTorch implementation of I2I-Mamba, a novel adversarial model for multi-modal medical image synthesis that leverages selective state space modeling (SSM) to efficiently capture long-range context while maintaining local precision. I2I-Mamba injects channel-mixed Mamba (cmMamba) blocks in the bottleneck of a convolutional backbone. In cmMamba blocks, SSM layers are used to learn context across the spatial dimension and channel-mixing layers are used to learn context across the channel dimension of feature maps.

architecture

Dependencies

python>=3.8.13
cuda=>11.6

torch>=2.2
torchvision>=0.17
visdom
dominate
scikit-image
h5py
scipy
ml_collections
mamba-ssm==1.1.3

You are welcome to ask any library issues.

Dataset

To reproduce the results reported in the paper, we recommend the following dataset processing steps:

Sequentially select subjects from the dataset. Apply skull-stripping to 3D volumes. Select 2D cross-sections from each subject. Normalize the selected 2D cross-sections before training and before metric calculation.

You should structure your aligned dataset in the following way:

/datasets/IXI/
  ├── T1_T2
  │   ├── train
  |    - 1.png
  |    - 2.png
  |    - ...
  │   ├── val
  │   └── test
  ├── T2_PD
  │   ├── train
  │   └── ...
  ├── T1_PD__T2
  │   ├── train
  │   └── ...

x.png is a single image containing both source and target image side-by-side. Thus the resulting image for one-to-one task will be 512 pixels horizontally and 256 pixels vertically and for many-to-one task it will be 768 pixels horizontally and 256 pixels vertically.

Note that for many-to-one tasks, source modalities should be in the Red and Green channels.

Training

Commands

One-to-one training, e.g. T2->PD

python3 train.py --dataroot datasets/IXI/T2_PD/ --name ixi_t2__pd --gpu_ids 0 --model i2i_mamba_one --which_model_netG i2i_mamba --which_direction AtoB --lambda_A 100 --dataset_mode aligned --norm batch --pool_size 0 --output_nc 1 --input_nc 1 --loadSize 256 --fineSize 256 --niter 30 --niter_decay 30 --save_epoch_freq 5 --checkpoints_dir checkpoints/ --display_id 0 --lr 0.0002

Many-to-one training, e.g. T1,T2->PD

python3 train.py --dataroot datasets/IXI/T1_T2__PD/ --name ixi_t1_t2__pd --gpu_ids 0 --model i2i_mamba_many --which_model_netG i2i_mamba --which_direction AtoB --lambda_A 100 --dataset_mode aligned --norm batch --pool_size 0 --output_nc 1 --input_nc 3 --loadSize 256 --fineSize 256 --niter 30 --niter_decay 30 --save_epoch_freq 5 --checkpoints_dir checkpoints/ --display_id 0 --lr 0.0002

Argument descriptions

Argument	Description
`--dataroot`	Root directory path of the dataset.
`--name`	Name of the experiment, used for storing model checkpoints and results.
`--gpu_ids`	List of GPU IDs to use for training (e.g., 0 for the first GPU).
`--model`	Model type to use for training.
`--which_model_netG`	Specifies the generator architecture to use.
`--which_direction`	Direction of the image translation, e.g., AtoB or BtoA.
`--lambda_A`	Weight for cycle consistency loss from domain A to B and back to A.
`--dataset_mode`	Dataset loading mode, specifying how the dataset is loaded (e.g., aligned).
`--norm`	Normalization type used in the model, e.g., batch normalization.
`--pool_size`	Size of the image buffer that stores previously generated images.
`--output_nc`	Number of output image channels.
`--input_nc`	Number of input image channels.
`--loadSize`	Scale images to this size before cropping.
`--fineSize`	Crop images to this size for training.
`--niter`	Number of iterations with the initial learning rate.
`--niter_decay`	Number of iterations to linearly decay the learning rate to zero.
`--save_epoch_freq`	Frequency of saving checkpoints at the end of epochs.
`--checkpoints_dir`	Directory where model checkpoints are saved.
`--display_id`	ID of the display window for visualizing training results (0 to disable).
`--lr`	Initial learning rate for the optimizer.

Testing

Commands

One-to-one testing, e.g. T2->PD

python3 test.py --dataroot datasets/IXI/T2_PD/ --name ixi_t2__pd --gpu_ids 0 --model i2i_mamba_one --which_model_netG i2i_mamba --dataset_mode aligned --norm batch --phase test --output_nc 1 --input_nc 1 --how_many 10000 --serial_batches --fineSize 256 --loadSize 256 --results_dir results/ --checkpoints_dir checkpoints/ --which_epoch 60

Many-to-one testing, e.g. T1,T2->PD

python3 test.py --dataroot datasets/IXI/T1_T2__PD/ --name T2_PD_T1_mambaoffTekrar_nonorm --gpu_ids 0 --model i2i_mamba_many --which_model_netG i2i_mamba --dataset_mode aligned --norm batch --phase test --output_nc 1 --input_nc 3 --how_many 10000 --serial_batches --fineSize 256 --loadSize 256 --results_dir results/ --checkpoints_dir checkpoints/ --which_epoch 60

Argument descriptions

Argument	Description
`--results_dir`	Directory where test results are saved.
`--serial_batches`	If set, takes images in order to make batches, otherwise takes them randomly.
`--how_many`	Number of test images to run.
`--which_epoch`	Which epoch to load? Set to 'latest' to use the latest cached model.

Citation

You are encouraged to modify/distribute this code. However, please acknowledge this code and cite the paper appropriately.

@article{atli2024i2imamba,
  title={I2I-Mamba: Multi-modal medical image synthesis via selective state space modeling}, 
  author={Omer F. Atli and Bilal Kabas and Fuat Arslan and Mahmut Yurt and Onat Dalmaz and Tolga Çukur},
  year={2024},
  journal={arXiv:2405.14022}
}

For any questions, comments and contributions, please feel free to contact Omer Faruk Atli (faruk.atli[at]bilkent.edu.tr)

Acknowledgments

This code uses libraries from ResViT and mamba repository.

<hr>