Abstract
Due to blurred boundaries between the background and the foreground, along with the overlapping of different tumor lesions, accurate segmentation of brain tumors presents significant challenges. To tackle these issues, we propose a causal intervention model designed for brain tumor segmentation. This model effectively eliminates the influence of irrelevant content on tumor region feature extraction, thereby enhancing segmentation precision. Notably, we adopt a front-door adjustment strategy to mitigate the confounding effects of MRI images on our segmentation outcomes. Our approach specifically targets the removal of background effects and interference in overlapping areas across tumor categories. Comprehensive experiments on the BraTS2020 and BraTS2021 datasets confirm the superior performance of our proposed method, demonstrating its effectiveness in improving accuracy in challenging segmentation scenarios.
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References
Baid, U., et al.: The rsna-asnr-miccai brats 2021 benchmark on brain tumor segmentation and radiogenomic classification. arXiv preprint arXiv:2107.02314 (2021)
Bakas, S.: Advancing the cancer genome atlas glioma mri collections with expert segmentation labels and radiomic features. Sci. Data 4(1), 1–13 (2017)
Bakas, S., et al.: Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the brats challenge. arXiv preprint arXiv:1811.02629 (2018)
Bauer, S., Wiest, R., Nolte, L.P., Reyes, M.: A survey of mri-based medical image analysis for brain tumor studies. Phys. Med. Biol. 58(13), R97 (2013)
Brügger, R., Baumgartner, C.F., Konukoglu, E.: A partially reversible U-net for memory-efficient volumetric image segmentation. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11766, pp. 429–437. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32248-9_48
Chen, C., Liu, X., Ding, M., Zheng, J., Li, J.: 3D dilated multi-fiber network for real-time brain tumor segmentation in MRI. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11766, pp. 184–192. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32248-9_21
Chen, J., et al.: 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: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 424–432. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46723-8_49
Dosovitskiy, A., et al.: An image is worth 16\(\times \)16 words: transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020)
Feng, Y., Cao, Y., An, D., Liu, P., Liao, X., Yu, B.: Daunet: a u-shaped network combining deep supervision and attention for brain tumor segmentation. Knowl.-Based Syst. 285, 111348 (2024)
Gordillo, N., Montseny, E., Sobrevilla, P.: State of the art survey on mri brain tumor segmentation. Magn. Reson. Imaging 31(8), 1426–1438 (2013)
Isensee, F., Jaeger, P.F., Kohl, S.A., Petersen, J., Maier-Hein, K.H.: nnu-net: a self-configuring method for deep learning-based biomedical image segmentation. Nat. Methods 18(2), 203–211 (2021)
Liu, Z., et al.: Canet: context aware network for brain glioma segmentation. IEEE Trans. Med. Imaging 40(7), 1763–1777 (2021)
Lotlikar, V.S., Satpute, N., Gupta, A.: Brain tumor detection using machine learning and deep learning: a review. Curr. Med. Imaging (2021)
Louis, D.N., et al.: The 2007 who classification of tumours of the central nervous system. Acta Neuropathol. 114(2), 97–109 (2007)
Luo, Z., Jia, Z., Yuan, Z., Peng, J.: Hdc-net: hierarchical decoupled convolution network for brain tumor segmentation. IEEE J. Biomed. Health Inf. 25(3), 737–745 (2020)
Menze, B.H., et al.: The multimodal brain tumor image segmentation benchmark (brats). IEEE Trans. Med. Imaging 34(10), 1993–2024 (2014)
Myronenko, A.: 3D MRI brain tumor segmentation using autoencoder regularization. In: Crimi, A., Bakas, S., Kuijf, H., Keyvan, F., Reyes, M., van Walsum, T. (eds.) BrainLes 2018. LNCS, vol. 11384, pp. 311–320. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-11726-9_28
Peiris, H., Hayat, M., Chen, Z., Egan, G., Harandi, M.: A robust volumetric transformer for accurate 3d tumor segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 162–172. Springer, Heidelberg (2022). https://doi.org/10.1007/978-3-031-16443-9_16
Sharma, N., Aggarwal, L.M.: Automated medical image segmentation techniques. J. Med. Phys./Assoc. Med. Physicists India 35(1), 3 (2010)
She, D., Zhang, Y., Zhang, Z., Li, H., Yan, Z., Sun, X.: Eoformer: Edge-oriented transformer for brain tumor segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 333–343. Springer, Heidelberg (2023). https://doi.org/10.1007/978-3-031-43901-8_32
Tian, Y., Bai, K., Yu, X., Zhu, S.: Causal multi-label learning for image classification. Neural Netw. 167, 626–637 (2023)
Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems. pp. 5998–6008 (2017)
Wang, T., Huang, J., Zhang, H., Sun, Q.: Visual commonsense r-cnn. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 10760–10770 (2020)
Wang, W., Chen, C., Ding, M., Yu, H., Zha, S., Li, J.: TransBTS: multimodal brain tumor segmentation using transformer. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12901, pp. 109–119. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-87193-2_11
Xing, Z., Yu, L., Wan, L., Han, T., Zhu, L.: Nestedformer: nested modality-aware transformer for brain tumor segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 140–150. Springer, Heidelberg (2022). https://doi.org/10.1007/978-3-031-16443-9_14
Xu, W., Yang, H., Zhang, M., Cao, Z., Pan, X., Liu, W.: Brain tumor segmentation with corner attention and high-dimensional perceptual loss. Biomed. Signal Process. Control 73, 103438 (2022)
Zeng, X., Zeng, P., Tang, C., Wang, P., Yan, B., Wang, Y.: Dbtrans: a dual-branch vision transformer for multi-modal brain tumor segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 502–512. Springer, Heidelberg (2023). https://doi.org/10.1007/978-3-031-43901-8_48
Zhang, M., et al.: Augmented transformer network for mri brain tumor segmentation. J. King Saud Univ.-Comput. Inf. Sci. 36(1), 101917 (2024)
Zhou, T., Canu, S., Vera, P., Ruan, S.: 3d medical multi-modal segmentation network guided by multi-source correlation constraint. In: 25th International Conference on Pattern Recognition, pp. 10243–10250. IEEE (2020)
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This work was supported in part by the National Natural Science Foundation of China (62272337).
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Liu, H., Li, Q., Nie, W., Xu, Z., Liu, A. (2024). Causal Intervention for Brain Tumor Segmentation. In: Linguraru, M.G., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2024. MICCAI 2024. Lecture Notes in Computer Science, vol 15009. Springer, Cham. https://doi.org/10.1007/978-3-031-72114-4_16
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DOI: https://doi.org/10.1007/978-3-031-72114-4_16
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