Abstract
Angiography is widely used to detect, diagnose, and treat cerebrovascular diseases. While numerous techniques have been proposed to segment the vascular network from different imaging modalities, deep learning (DL) has emerged as a promising approach. However, existing DL methods often depend on proprietary datasets and extensive manual annotation. Moreover, the availability of pre-trained networks specifically for medical domains and 3D volumes is limited. To overcome these challenges, we propose a few-shot learning approach called “VesselShot” for cerebrovascular segmentation. VesselShot leverages knowledge from a few annotated support images and mitigates the scarcity of labeled data and the need for extensive annotation in cerebral blood vessel segmentation. We evaluated the performance of VesselShot using the publicly available TubeTK dataset for the segmentation task, achieving a mean Dice coefficient (DC) of \(0.62\pm 0.03\).
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References
Goni, M.R., Ruhaiyem, N.I.R., Mustapha, M., Achuthan, A., Nassir, C.M.N.C.M.: Brain vessel segmentation using deep learning-a review. In: IEEE Access (2022)
Roy, A.G., Siddiqui, S., Pölsterl, S., Navab, N., Wachinger, C.: squeeze & excite’guided few-shot segmentation of volumetric images. Med. Image Anal. 59, 101587 (2020)
Tang, H., Liu, X., Sun, S., Yan, X., Xie, X.: Recurrent mask refinement for few-shot medical image segmentation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 3918–3928 ( 2021)
Ouyang, C., Biffi, C., Chen, C., Kart, T., Qiu, H., Rueckert, D.: Self-supervision with superpixels: training few-shot medical image segmentation without annotation. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12374, pp. 762–780. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58526-6_45
Mondal, A.K., Dolz, J., Desrosiers, C.: Few-shot 3d multi-modal medical image segmentation using generative adversarial learning. arXiv preprint arXiv:1810.12241 (2018)
Guo, S., Xu, L., Feng, C., Xiong, H., Gao, Z., Zhang, H.: Multi-level semantic adaptation for few-shot segmentation on cardiac image sequences. Med. Image Anal. 73, 102170 (2021)
Xu, J., et al.: A few-shot learning-based retinal vessel segmentation method for assisting in the central serous chorioretinopathy laser surgery. Front. Med. 9, 821565 (2022)
Wang, K., Liew, J.H., Zou, Y., Zhou, D., Feng, J.: PANet: few-shot image semantic segmentation with prototype alignment. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9197–9206 (2019)
Wang, Y., et al.: VC-Net: deep volume-composition networks for segmentation and visualization of highly sparse and noisy image data. IEEE Trans. Visual Comput. Graph. 27(2), 1301–1311 (2020)
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)
Su, J., et al.: DV-Net: accurate liver vessel segmentation via dense connection model with D-BCE loss function. Knowl. Based Syst. 232, 107471 (2021)
Livne, M., et al.: A u-net deep learning framework for high performance vessel segmentation in patients with cerebrovascular disease. Front. Neurosci. 13, 97 (2019)
Hellum, O., Mu, Y., Kersten-Oertel, M., Xiao, Y.: A novel prototype for virtual-reality-based deep brain stimulation trajectory planning using voodoo doll annotation and eye-tracking. Comput. Methods Biomech. Biomed. Eng. Imaging Visual. 10(4), 418–424 (2022)
Bériault, S., et al.: Towards computer-assisted deep brain stimulation targeting with multiple active contacts. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012. LNCS, vol. 7510, pp. 487–494. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-33415-3_60
Li, M., Li, S., Han, Y., Zhang, T.: GVC-Net: global vascular context network for cerebrovascular segmentation using sparse labels. IRBM 43(6), 561–572 (2022)
Holroyd, N.A., Li, Z., Walsh, C., Brown, E.E., Shipley, R.J., Walker-Samuel, S.: tUbe net: a generalizable deep learning tool for 3d vessel segmentation, pp. 2023–07. bioRxiv (2023)
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This study was funded by an FRQNT Team Grant (2022-PR-296459).
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Aktar, M., Rivaz, H., Kersten-Oertel, M., Xiao, Y. (2023). VesselShot: Few-shot Learning for Cerebral Blood Vessel Segmentation. In: Abdulkadir, A., et al. Machine Learning in Clinical Neuroimaging. MLCN 2023. Lecture Notes in Computer Science, vol 14312. Springer, Cham. https://doi.org/10.1007/978-3-031-44858-4_5
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DOI: https://doi.org/10.1007/978-3-031-44858-4_5
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