Abstract
Vessel segmentation and anatomical labeling are of great significance for vascular disease analysis. Because vessels in 3D images are the tree-like tubular structures with diverse shapes and sizes, and direct use of convolutional neural networks (CNNs, based on spatial convolutional kernels) for vessel segmentation often encounters great challenges. To tackle this problem, we propose a graph convolutional network (GCN)-based point cloud approach to improve vessel segmentation over the conventional CNN-based method and further conduct semantic labeling on 13 major head and neck vessels. The proposed method can not only learn the global shape representation but also precisely adapt to local vascular shapes by utilizing the prior knowledge of tubular structures to explicitly learn anatomical shape. Specifically, starting from rough segmentation using V-Net, our approach further refines the segmentation and performs labeling on the refined segmentations, with two steps. First, a point cloud network is applied to the points formed by initial vessel voxels to refine vessel segmentation. Then, GCN is employed on the point cloud to further label vessels into 13 major segments. To evaluate the performance of our proposed method, CT angiography images (covering heads and necks) of 72 subjects are used in our experiment. Using four-fold cross-validation, an average Dice coefficient of 0.965 can be achieved for vessel segmentation compared to that of 0.885 obtained by the conventional V-Net based segmentation. Also, for vessel labeling, our proposed algorithm achieves an average Dice coefficient of 0.899 for 13 vessel segments compared to that of 0.829 by V-Net. These results show that our proposed method could facilitate head and neck vessel analysis by providing automatic and accurate vessel segmentation and labeling results.
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References
Hedblom, A.: Blood vessel segmentation for neck and head computed tomography angiography (2013)
Cuisenaire, O., Virmani, S., Olszewski, M.E., Ardon, R.: Fully automated segmentation of carotid and vertebral arteries from contrast-enhanced CTA. In: Medical Imaging 2008: Image Processing, vol. 6914, p. 69143R. International Society for Optics and Photonics (2008)
Bogunovic, H., Pozo, J.M., Cardenes, R., San Roman, L., Frangi, A.F.: Anatomical labeling of the circle of willis using maximum a posteriori probability estimation. IEEE Trans. Medi. Imaging 32(9), 1587–1599 (2013)
Robben, D., et al.: Simultaneous segmentation and anatomical labeling of the cerebral vasculature. Med. Image Anal. 32, 201–215 (2016)
Moccia, S., De Momi, E., El Hadji, S., Mattos, L.S.: Blood vessel segmentation algorithms-review of methods, datasets and evaluation metrics. Comput. Methods Programs Biomed. 158, 71–91 (2018)
Liu, W., Sun, J., Li, W., Hu, T., Wang, P.: Deep learning on point clouds and its application: a survey. Sensors 19(19), 4188 (2019)
Liu, Y., Fan, B., Xiang, S., Pan, C.: Relation-shape convolutional neural network for point cloud analysis. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 8895–8904 (2019)
Liu, Z., Tang, H., Lin, Y., Han, S.: Point-voxel CNN for efficient 3D deep learning. In: Advances in Neural Information Processing Systems, pp. 963–973 (2019)
Qi, C.R., Su, H., Mo, K., Guibas, L.J.: Pointnet: deep learning on point sets for 3D classification and segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 652–660 (2017)
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. LNCS, vol. 11765, pp. 273–281. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32245-8_31
Garcia-Uceda Juarez, A., Selvan, R., Saghir, Z., de Bruijne, M.: A joint 3D UNet-graph neural network-based method for airway segmentation from chest CTs. In: Suk, H.-I., Liu, M., Yan, P., Lian, C. (eds.) MLMI 2019. LNCS, vol. 11861, pp. 583–591. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32692-0_67
Shin, S.Y., Lee, S., Yun, I.D., Lee, K.M.: Deep vessel segmentation by learning graphical connectivity. Med. Image Anal. 58, 101556 (2019)
Wolterink, J.M., Leiner, T., Išgum, I.: Graph convolutional networks for coronary artery segmentation in cardiac CT angiography. In: Zhang, D., Zhou, L., Jie, B., Liu, M. (eds.) GLMI 2019. LNCS, vol. 11849, pp. 62–69. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-35817-4_8
Zhai, Z., et al.: Linking convolutional neural networks with graph convolutional networks: application in pulmonary artery-vein separation. In: Zhang, D., Zhou, L., Jie, B., Liu, M. (eds.) GLMI 2019. LNCS, vol. 11849, pp. 36–43. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-35817-4_5
Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)
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)
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Yao, L. et al. (2020). Graph Convolutional Network Based Point Cloud for Head and Neck Vessel Labeling. In: Liu, M., Yan, P., Lian, C., Cao, X. (eds) Machine Learning in Medical Imaging. MLMI 2020. Lecture Notes in Computer Science(), vol 12436. Springer, Cham. https://doi.org/10.1007/978-3-030-59861-7_48
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DOI: https://doi.org/10.1007/978-3-030-59861-7_48
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