Abstract
Medical image segmentation plays a pivotal role in computer-aided diagnosis and treatment planning. Traditional segmentation approaches often struggle to balance global and local context, either capturing overall anatomical structures or focusing on minute details, but not both. This paper introduces the Swin-Hierarchical Attention Unet (Swin-HAUnet), which harmonizes this dichotomy by integrating global contextual insights with local feature enhancement. The proposed network architecture employs a hybrid approach, leveraging an advanced transformer-based encoder to process wide-ranging contextual information and an attention-enhanced decoder to refine the segmentation of nuanced and intricate anatomical features. We performed experiments on two publicly available datasets, the Synapse multi-organ segmentation CT dataset and the UW-Madison dataset. The Swin-HAUnet shows a marked improvement in performance, achieving a Dice similarity coefficient of 79.91%, a notable increase of 1.26% over the baseline model on Synapse datasets. These results underscore the model’s effectiveness in complex segmentation tasks and the importance of attention mechanisms in medical image analysis.
Jiarong Chen, Xuyang Zhang, and Rongwen Li are co-first authors.
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References
Cai, S., Tian, Y., Lui, H., Zeng, H., Wu, Y., Chen, G.: Dense-Unet: a novel multiphoton in vivo cellular image segmentation model based on a convolutional neural network. Quant. Imaging Med. Surg. 10(6), 1275 (2020)
Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-Unet: Unet-like pure transformer for medical image segmentation. In: ECCV, pp. 205–218. Springer (2022)
Chang, Y., Menghan, H., Guangtao, Z., Xiao-Ping, Z.: Transclaw u-net: Claw u-net with transformers for medical image segmentation. arXiv preprint arXiv:2107.05188 (2021)
Chen, J., Lu, Y., Yu, Q., Luo, X., Adeli, E., Wang, Y., Lu, L., Yuille, A.L., Zhou, Y.: Transunet: Transformers make strong encoders for medical image segmentation. arXiv preprint arXiv:2102.04306 (2021)
Çiçek, Ö., Abdulkadir, A., Lienkamp, S.S., Brox, T., Ronneberger, O.: 3d u-net: learning dense volumetric segmentation from sparse annotation. In: MICCAI 2016, pp. 424–432. Springer (2016)
Ding, F., Yang, G., Liu, J., Wu, J., Ding, D., Xv, J., Cheng, G., Li, X.: Hierarchical attention networks for medical image segmentation. arXiv preprint arXiv:1911.08777 (2019)
Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020)
Fu, S., Lu, Y., Wang, Y., Zhou, Y., Shen, W., Fishman, E., Yuille, A.: Domain adaptive relational reasoning for 3d multi-organ segmentation. In: MICCAI 2020, pp. 656–666. Springer (2020)
Gumaei, A., Hassan, M.M., Hassan, M.R., Alelaiwi, A., Fortino, G.: A hybrid feature extraction method with regularized extreme learning machine for brain tumor classification. IEEE Access 7, 36266–36273 (2019)
Hu, Q., Chen, Y., Xiao, J., Sun, S., Chen, J., Yuille, A.L., Zhou, Z.: Label-free liver tumor segmentation. In: Proceedings of the IEEE/CVF CVPR, pp. 7422–7432 (2023)
Liu, W., Li, D., Su, H.: Hana: Hierarchical attention network assembling for semantic segmentation. Cogn. Comput. 13(5), 1128–1135 (2021)
Liu, Y., Wu, Y.H., Sun, G., Zhang, L., Chhatkuli, A., Van Gool, L.: Vision transformers with hierarchical attention. arXiv preprint arXiv:2106.03180 (2021)
Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B.: Swin transformer: Hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE/CVF ICCV, pp. 10012–10022 (2021)
Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B.: Swin transformer: Hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE/CVF ICCV, pp. 10012–10022 (2021)
Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on CVPR, pp. 3431–3440 (2015)
Milletari, F., Navab, N., Ahmadi, S.A.: V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 Fourth International Conference on 3D Vision (3DV), pp. 565–571. IEEE (2016)
Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: MICCAI 2015, pp. 234–241. Springer (2015)
Salehi, S.S.M., Erdogmus, D., Gholipour, A.: Tversky loss function for image segmentation using 3d fully convolutional deep networks. In: International Workshop on Machine Learning in Medical Imaging, pp. 379–387. Springer (2017)
Shu, Z., Entezari, A.: Sparse-view and limited-angle CT reconstruction with untrained networks and deep image prior. Comput. Methods Programs Biomed. 226, 107167 (2022)
Siddique, N., Paheding, S., Elkin, C.P., Devabhaktuni, V.: U-net and its variants for medical image segmentation: A review of theory and applications. IEEE Access 9, 82031–82057 (2021)
Tan, M., Le, Q.: Efficientnet: Rethinking model scaling for convolutional neural networks. In: International Conference on Machine Learning, pp. 6105–6114. PMLR (2019)
Wang, J., Zhou, P., Han, X., Chen, Y.: Medical image super-resolution via diagnosis-guided attention. In: 2023 IEEE International Conference on Multimedia and Expo (ICME), pp. 462–467 (2023)
Wang, Z., Yin, Y., Shi, J., Fang, W., Li, H., Wang, X.: Zoom-in-net: Deep mining lesions for diabetic retinopathy detection. In: MICCAI 2017, pp. 267–275. Springer (2017)
Woo, S., Park, J., Lee, J.Y., Kweon, I.S.: Cbam: Convolutional block attention module. In: ECCV, pp. 3–19 (2018)
Xiao, X., Lian, S., Luo, Z., Li, S.: Weighted res-unet for high-quality retina vessel segmentation. In: 2018 9th International Conference on Information Technology in Medicine and Education (ITME), pp. 327–331. IEEE (2018)
Yang, Z., Yang, D., Dyer, C., He, X., Smola, A., Hovy, E.: Hierarchical attention networks for document classification. In: Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 1480–1489 (2016)
Yuan, Y., Zhang, L., Wang, L., Huang, H.: Multi-level attention network for retinal vessel segmentation. IEEE J. Biomed. Health Inform. 26(1), 312–323 (2021)
Zhao, L., Peng, X., Tian, Y., Kapadia, M., Metaxas, D.N.: Semantic graph convolutional networks for 3d human pose regression. In: Proceedings of the IEEE/CVF CVPR, pp. 3425–3435 (2019)
Zhou, F., Luo, F., Efio-Akolly, K., Bbosa, R., Huang, W.C., Zou, J.N., Chen, Y.P.P., Liu, F.: Haunet-3d: a novel hierarchical attention 3D Unet for lung nodule segmentation. In: 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1213–1220. IEEE (2021)
Zhou, Z., Rahman Siddiquee, M.M., Tajbakhsh, N., Liang, J.: Unet++: a nested u-net architecture for medical image segmentation. In: Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, pp. 3–11. Springer (2018)
Acknowledgements
This work is supported by the National Natural Science Foundation of China grants 62176001 and Natural Science Project of Anhui Provincial Education Department grants 2023AH030004.
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Chen, J., Zhang, X., Li, R., Zhou, P. (2025). Swin-HAUnet: A Swin-Hierarchical Attention Unet For Enhanced Medical Image Segmentation. In: Lin, Z., et al. Pattern Recognition and Computer Vision. PRCV 2024. Lecture Notes in Computer Science, vol 15044. Springer, Singapore. https://doi.org/10.1007/978-981-97-8496-7_26
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