# SelfRDB **Repository Path**: yoveria/SelfRDB ## Basic Information - **Project Name**: SelfRDB - **Description**: SelfRDB 的 PyTorch 实现，这是一种用于多模态医学图像合成的扩散桥模型 - **Primary Language**: Unknown - **License**: MIT - **Default Branch**: main - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2025-12-21 - **Last Updated**: 2026-03-22 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README

SelfRDB
Self-Consistent Recursive Diffusion Bridge for Medical Image Translation

Fuat Arslan1,2   ·   Bilal Kabas1,2   ·   Onat Dalmaz3   ·   Muzaffer Ozbey4   ·   Tolga Çukur1,2   1Bilkent University   2UMRAM   3Stanford University   4University of Illinois Urbana-Champaign

[arXiv]   ·   [Journal (Medical Image Analysis)]

Official PyTorch implementation of **SelfRDB**, a novel diffusion bridge model for multi-modal medical image synthesis that employs a novel forward process with soft-prior, and self-consistent recursion in reverse process. Our novel noise scheduling with monotonically increasing variance towards the end-point, i.e. soft-prior, boosts generalization performance and facilitates information transfer between the two modalities. To further enhance sampling accuracy in each reverse step, SelfRDB utilizes a novel sampling procedure where the network recursively generates a transient-estimate of the target image until convergence onto a self-consistent solution.

architecture

## ⚙️ Installation This repository has been developed and tested with `CUDA 11.7` and `Python 3.8`. Below commands create a conda environment with required packages. Make sure conda is installed. ``` conda env create --file requirements.yaml conda activate selfrdb ``` ## 🗂️ Prepare dataset The default data set class `NumpyDataset` requires the following folder structure to organize the data set. Modalities (T1, T2, etc.) are separated by folders, splits (train, val, test) are organized as subfolders which include 2D images: `slice_0.npy`, `slice_1.npy`, ... To use your custom data set class, set `dataset_class` to your own implementation in `dataset.py` by inheriting from the `BaseDataset` class. > Images should be scaled to have pixel values in the range [0,1]. ``` / ├── / │ ├── train/ │ │ ├── slice_0.npy │ │ ├── slice_1.npy │ │ └── ... │ ├── test/ │ │ ├── slice_0.npy │ │ └── ... │ └── val/ │ ├── slice_0.npy │ └── ... ├── / │ ├── train/ │ ├── test/ │ └── val/ ├── ... ``` ## 🏃 Training Run the following command to start/resume training. Model checkpoints are saved under `logs/$EXP_NAME/version_x/checkpoints` directory, and sample validation images are saved under `logs/$EXP_NAME/version_x/val_samples`. The script supports both single and multi-GPU training. By default, it runs on a single GPU. To enable multi-GPU training, set `--trainer.devices` argument to the list of devices, e.g. `0,1,2,3`. ``` python main.py fit \ --config config.yaml \ --trainer.logger.name $EXP_NAME \ --data.dataset_dir $DATA_DIR \ --data.source_modality $SOURCE \ --data.target_modality $TARGET \ --data.train_batch_size $BS_TRAIN \ --data.val_batch_size $BS_VAL \ [--trainer.max_epoch $N_EPOCHS] \ [--ckpt_path $CKPT_PATH] \ [--trainer.devices $DEVICES] ``` ### Argument descriptions | Argument | Description | |-----------------------------|--------------------------------------------------------------------------------------------------------------------------------| | `--config` | Config file path. | | `--trainer.logger.name` | Experiment name. | | `--data.dataset_dir` | Data set directory. | | `--data.source_modality` | Source modality, e.g. 'T1', 'T2', 'PD'. Should match the folder name for that modality. | | `--data.train_batch_size` | Train set batch size. | | `--data.val_batch_size` | Validation set batch size. | | `--trainer.max_epoch` | [Optional] Number of training epochs (default: 50). | | `--ckpt_path` | [Optional] Model checkpoint path to resume training. | | `--trainer.devices` | [Optional] Device or list of devices. For multi-GPU set to the list of device ids, e.g `0,1,2,3` (default: `[0]`). | ## 🧪 Testing Run the following command to start testing. The predicted images are saved under `logs/$EXP_NAME/version_x/test_samples` directory. By default, the script runs on a single GPU. To enable multi-GPU testing, set `--trainer.devices` argument to the list of devices, e.g. `0,1,2,3`. ``` python main.py test \ --config config.yaml \ --data.dataset_dir $DATA_DIR \ --data.source_modality $SOURCE \ --data.target_modality $TARGET \ --data.test_batch_size $BS_TEST \ --ckpt_path $CKPT_PATH ``` ### Argument descriptions Some arguments are common to both training and testing and are not listed here. For details on those arguments, please refer to the training section. | Argument | Description | |-----------------------------|--------------------------------------------| | `--data.test_batch_size` | Test set batch size. | | `--ckpt_path` | Model checkpoint path. | ## 🦁 Model Zoo Refer to the testing section above to perform inference with the checkpoints. PSNR (dB) and SSIM (%) are listed as mean ± std across the test set. | Dataset | Task | PSNR | SSIM | Checkpoint | |-----------|-----------|--------------------|---------------------|---------------------------------------------------------------------------------------------------------| | IXI | T2→T1 | 31.63 ± 1.53 | 95.64 ± 1.12 | [Link](https://github.com/icon-lab/SelfRDB/releases/download/v1.0.0/ixi_t2_t1.ckpt) | | IXI | T1→T2 | 31.28 ± 1.56 | 95.03 ± 1.27 | [Link](https://github.com/icon-lab/SelfRDB/releases/download/v1.0.0/ixi_t1_t2.ckpt) | | IXI | PD→T1 | 31.23 ± 1.22 | 95.64 ± 0.99 | [Link](https://github.com/icon-lab/SelfRDB/releases/download/v1.0.0/ixi_pd_t1.ckpt) | | IXI | T1→PD | 32.17 ± 1.57 | 95.15 ± 0.99 | [Link](https://github.com/icon-lab/SelfRDB/releases/download/v1.0.0/ixi_t1_pd.ckpt) | | BRATS | T2→T1 | 28.85 ± 1.48 | 93.70 ± 1.87 | [Link](https://github.com/icon-lab/SelfRDB/releases/download/v1.0.0/brats_t2_t1.ckpt) | | BRATS | T1→T2 | 27.58 ± 1.88 | 92.99 ± 2.44 | [Link](https://github.com/icon-lab/SelfRDB/releases/download/v1.0.0/brats_t1_t2.ckpt) | | BRATS | FLAIR→T2 | 26.85 ± 1.75 | 91.66 ± 2.72 | [Link](https://github.com/icon-lab/SelfRDB/releases/download/v1.0.0/brats_flair_t2.ckpt) | | BRATS | T2→FLAIR | 27.98 ± 1.80 | 90.01 ± 2.70 | [Link](https://github.com/icon-lab/SelfRDB/releases/download/v1.0.0/brats_t2_flair.ckpt) | | CT | T2→CT | 29.18 ± 2.18 | 93.28 ± 1.99 | [Link](https://github.com/icon-lab/SelfRDB/releases/download/v1.0.0/ct_t2_ct.ckpt) | | CT | T1→CT | 27.55 ± 3.32 | 92.29 ± 6.32 | [Link](https://github.com/icon-lab/SelfRDB/releases/download/v1.0.0/ct_t1_ct.ckpt) | ## ✒️ Citation You are encouraged to modify/distribute this code. However, please acknowledge this code and cite the paper appropriately. ``` @article{arslan2024selfconsistent, title={Self-Consistent Recursive Diffusion Bridge for Medical Image Translation}, author={Fuat Arslan and Bilal Kabas and Onat Dalmaz and Muzaffer Ozbey and Tolga Çukur}, year={2024}, journal={arXiv:2405.06789} } ```
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