Unleashing Diffusion and State Space Models for Medical Image Segmentation.
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It is recommended to clone the repository with Python 3.9:
git clone https://github.com/Rows21/k-Means_Mask_Mamba.git cd k-Means_Mask_Mamba
The data that support the findings of this study are openly available as follows:
- 01 Multi-Atlas Labeling Beyond the Cranial Vault - Workshop and Challenge (BTCV), reference [57].
- 02 Pancreas-CT TCIA, reference [69].
- 03 Combined Healthy Abdominal Organ Segmentation (CHAOS), reference [54].
- 04 Liver Tumor Segmentation Challenge (LiTS), reference [45].
- 05 Kidney and Kidney Tumor Segmentation (KiTS), reference [50].
- 06 WORD: A large scale dataset, benchmark and clinical applicable study for abdominal organ segmentation from CT image, reference [61].
- 07 AbdomenCT-1K, reference [62].
- 08 Multi-Modality Abdominal Multi-Organ Segmentation Challenge (AMOS), reference [53].
- 09 MSD, reference [43].
- 10 CT volumes with multiple organ segmentations (CT-ORG), reference [68].
- 11 TotalSegmentator, reference [73].
- Please refer to CLIP-Driven to organize the downloaded datasets.
- Modify ORGAN_DATASET_DIR and NUM_WORKER in label_transfer.py
python -W ignore label_transfer.py
If you find this repository helpful, please consider citing:
@article{wu2025unleashing,
title={Unleashing Diffusion and State Space Models for Medical Image Segmentation},
author={Wu, Rong and Chen, Ziqi and Zhong, Liming and Li, Heng and Shu, Hai},
journal={arXiv preprint arXiv:2506.12747},
year={2025}
}
This repository is built using the timm library.
[43] Antonelli, M., Reinke, A., Bakas, S., Farahani, K., Kopp-Schneider, A., Landman, B. A., et al. (2022). The medical segmentation decathlon. Nature Communications, 13(1), 4128.
[45] Bilic, P., Christ, P., Li, H. B., Vorontsov, E., Ben-Cohen, A., Kaissis, G., et al. (2023). The liver tumor segmentation benchmark (LiTS). Medical Image Analysis, 84, 102680.
[50] Heller, N., McSweeney, S., Peterson, M. T., et al. (2020). An international challenge to use artificial intelligence to define the state-of-the-art in kidney and kidney tumor segmentation in CT imaging. J. Clin. Oncol., 38(6 Suppl), 626.
[53] Ji, Y., Bai, H., Ge, C., Yang, J., Zhu, Y., Zhang, R., et al. (2022). Amos: A large-scale abdominal multi-organ benchmark for versatile medical image segmentation. NeurIPS, 35:36722–36732.
[54] Kavur, A. E., Gezer, N. S., Barış, M., Aslan, S., Conze, P. H., Groza, V., et al. (2021). Chaos challenge-combined (ct-mr) healthy abdominal organ segmentation. Medical Image Analysis, 69, 101950.
[57] Landman, B., Xu, Z., Iglesias, J., Styner, M., Langerak, T., Klein, A. (2015). MICCAI multi-atlas labeling beyond the cranial vault–workshop and challenge. In MICCAI Challenge, vol. 5, p. 12.
[61] Luo, X., Liao, W., Xiao, J., Chen, J., Song, T., Zhang, X., et al. (2022). WORD: A large-scale dataset, benchmark and clinical applicable study for abdominal organ segmentation from CT image. Medical Image Analysis, 82, 102642.
[62] Ma, J., Zhang, Y., Gu, S., Zhu, C., Ge, C., Zhang, Y. (2021). AbdomenCT-1K: Is abdominal organ segmentation a solved problem? TPAMI, 44(10):6695–6714.
[68] Rister, B., Yi, D., Shivakumar, K., Nobashi, T., Rubin, D. L. (2020). CT-ORG, a new dataset for multiple organ segmentation in computed tomography. Scientific Data, 7(1):381.
[69] Roth, H. R., Lu, L., Farag, A., Shin, H. C., Liu, J., Turkbey, E. B., et al. (2015). DeepOrgan: Multi-level deep convolutional networks for automated pancreas segmentation. In MICCAI, pp. 556–564.
[73] Wasserthal, J., Breit, H.-C., Meyer, M. T., Pradella, M., Hinck, D., Sauter, A. W., et al. (2023). TotalSegmentator: Robust segmentation of 104 anatomic structures in CT images. Radiology: Artificial Intelligence, 5(5):e230024.
Shield:
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
© 2025 Rong Wu. You are free to share and adapt the material with attribution.