Abstract
Acquiring accurate anatomy information of intracranial artery from 3D digital subtraction angiography (3D-DSA) is crucial for intracranial artery intervention surgery. However, this task often comes with challenges of large-scale image and memory constraints. In this paper, an effective two-stage framework is proposed for fully automatic morphological analysis of intracranial artery. In the first stage, the proposed Region-Global Fusion Network (RGFNet) achieves accurate and continuous segmentation of intracranial artery. In the second stage, the 3D morphological analysis algorithm obtains the access diameter, the minimum inner diameter and the minimum radius of curvature of intracranial artery. RGFNet achieves state-of-the-art performance (93.36% in Dice, 87.83% in mIoU and 15.64 in HD95) in the 3D-DSA intracranial artery segmentation dataset, and the proposed morphological analysis algorithm also shows effectiveness in obtaining accurate anatomy information. The proposed framework is not only helpful for surgeons to plan the procedures of interventional surgery but also promising to be integrated to robotic navigation systems, enabling robotic-assisted surgery.
H. Zhao and T. Wang—Co-first authors.
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References
Nassef, A.M., Awad, E.M., El-bassiouny, A.A., et al.: Endovascular stenting of medically refractory intracranial arterial stenotic (ICAS) disease (clinical and sonographic study). Egypt. J. Neurol. Psychiatry Neurosurg. 56(1), 1–12 (2020)
Gao, P., Wang, T., Wang, D., et al.: Effect of stenting plus medical therapy vs medical therapy alone on risk of stroke and death in patients with symptomatic intracranial stenosis: the CASSISS randomized clinical trial. J. Am. Med. Assoc. 328(6), 534–542 (2022)
Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W., Frangi, A. (eds.) MICCAI 2015, Part III. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28
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., Unal, G., Wells, W. (eds.) MICCAI 2016, Part II. LNCS, vol. 9901, pp. 424–432. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46723-8_49
Balsiger, F., Soom, Y., Scheidegger, O., Reyes, M.: Learning shape representation on sparse point clouds for volumetric image segmentation. In: Shen, D., et al. (eds.) MICCAI 2019, Part II. LNCS, vol. 11765, pp. 273–281. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32245-8_31
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 (2016)
Chen, H., Dou, Q., Yu, L., et al.: VoxResNet: deep voxelwise residual networks for volumetric brain segmentation. arXiv preprint arXiv:1608.05895 (2016)
Chen, J., Lu, Y., Yu, Q., et al.: TransUNet: transformers make strong encoders for medical image segmentation. arXiv preprint arXiv:2102.04306 (2021)
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, Part I. LNCS vol. 12901, pp. 109–119. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-87193-2_11
Hatamizadeh, A., Tang, Y., Nath, V., et al.: UNETR: transformers for 3D medical image segmentation. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 574–584 (2022)
Tang, Y., Yang, D., Li, W., et al.: Self-supervised pre-training of swin transformers for 3D medical image analysis. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 20730–20740 (2022)
Dosovitskiy A., Beyer, L., Kolesnikov, A., et al.: An image is worth 16x16 words: transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020)
Liu, Z., Lin, Y., Cao, Y., et al.: Swin transformer: hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pp. 10012–10022 (2021)
Cao, H., et al.: Swin-Unet: Unet-like pure transformer for medical image segmentation. In: Karlinsky, L., Michaeli, T., Nishino, K. (eds.) ECCV 2022, Part III. LNCS vol. 13803, pp. 205–218. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-25066-8_9
Peiris, H., Hayat, M., Chen, Z., Egan, G., Harandi, M.: A robust volumetric transformer for accurate 3D tumor segmentation. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds.) MICCAI 2022, Part V. LNCS, vol. 13435, pp. 162–172. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-16443-9_16
Palágyi, K., et al.: A sequential 3D thinning algorithm and its medical applications. In: Insana, M.F., Leahy, R.M. (eds.) IPMI 2001. LNCS, vol. 2082, pp. 409–415. Springer, Heidelberg (2001). https://doi.org/10.1007/3-540-45729-1_42
Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101 (2017)
Acknowledgements
This work was supported in part by the National Key Research and Development Program of China under Grant (2022YFB4700902), the National High Level Hospital Clinical Research Funding (2022-PUMCH-B-125), and the National Natural Science Foundation of China under Grant (62303463, U1913210, 62073325, 62003343, 62222316).
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Zhao, H. et al. (2024). An Effective Morphological Analysis Framework of Intracranial Artery in 3D Digital Subtraction Angiography. In: Luo, B., Cheng, L., Wu, ZG., Li, H., Li, C. (eds) Neural Information Processing. ICONIP 2023. Communications in Computer and Information Science, vol 1964. Springer, Singapore. https://doi.org/10.1007/978-981-99-8141-0_4
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