Abstract
Pulmonary vessel segmentation is important for clinical diagnosis of pulmonary diseases, while is also challenging due to the complicated structure. In this work, we present an effective framework and refinement process of pulmonary vessel segmentation from chest computed tomographic (CT) images. The key to our approach is a 2.5D segmentation network applied from three orthogonal axes, which presents a robust and fully automated pulmonary vessel segmentation result with lower network complexity and memory usage compared to 3D networks. The slice radius is introduced to convolve the adjacent information of the center slice and the multi-planar fusion optimizes the presentation of intra and inter slice features. Besides, the tree-like structure of pulmonary vessel is extracted in the post-processing process, which is used for segmentation refining and pruning. In the evaluation experiments, three fusion methods are tested and the most promising one is compared with the state-of-the-art 2D and 3D structures on 300 cases of lung images randomly selected from LIDC dataset. Our method outperforms other network structures by a large margin and achieves by far the highest average DICE score of 0.9272 and a precision of 0.9310, as per our knowledge from the pulmonary vessel segmentation models available in literature.
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References
Armato, S.G., et al.: The lung image database consortium (LIDC) and image database resource initiative (IDRI): a completed reference database of lung nodules on CT scans. Med. Phys. 38(2), 915–931 (2011)
El-Baz, A., Suri, J.S.: Lung Imaging and Computer Aided Diagnosis. CRC Press, Boca Raton (2011)
Kaftan, J.N., Kiraly, A.P., Bakai, A., Das, M., Novak, C.L., Aach, T.: Fuzzy pulmonary vessel segmentation in contrast enhanced CT data. In: Medical Imaging 2008: Image Processing, vol. 6914, p. 69141Q. International Society for Optics and Photonics (2008)
Lesage, D., Angelini, E.D., Bloch, I., Funka-Lea, G.: A review of 3D vessel lumen segmentation techniques: models, features and extraction schemes. Med. Image Anal. 13(6), 819–845 (2009)
Li, X., Chen, H., Qi, X., Dou, Q., Fu, C.W., Heng, P.A.: H-DenseUNet: hybrid densely connected UNet for liver and tumor segmentation from CT volumes. IEEE Trans. Med. Imaging 37(12), 2663–2674 (2018)
Rudyanto, R.D., et al.: Comparing algorithms for automated vessel segmentation in computed tomography scans of the lung: the VESSEL12 study. Med. Image Anal. 18(7), 1217–1232 (2014)
Schneider, M., Reichold, J., Weber, B., Székely, G., Hirsch, S.: Tissue metabolism driven arterial tree generation. Med. Image Anal. 16(7), 1397–1414 (2012)
Shang, Y., et al.: Vascular active contour for vessel tree segmentation. IEEE Trans. Biomed. Eng. 58(4), 1023–1032 (2011)
Shikata, H., Hoffman, E.A., Sonka, M.: Automated segmentation of pulmonary vascular tree from 3D CT images. In: Medical Imaging 2004: Physiology, Function, and Structure from Medical Images, vol. 5369, pp. 107–117. International Society for Optics and Photonics (2004)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)
Staal, J., Abràmoff, M.D., Niemeijer, M., Viergever, M.A., Van Ginneken, B.: Ridge-based vessel segmentation in color images of the retina. IEEE Trans. Med. Imaging 23(4), 501–509 (2004)
Tetteh, et al.: DeepVesselNet: vessel segmentation, centerline prediction, and bifurcation detection in 3-D angiographic volumes. arXiv preprint arXiv:1803.09340 (2018)
Yun, J., et al.: Improvement of fully automated airway segmentation on volumetric computed tomographic images using a 2.5 dimensional convolutional neural net. Med. Image Anal. 51, 13–20 (2019)
Zhou, Z., Rahman Siddiquee, M.M., Tajbakhsh, N., Liang, J.: UNet++: a nested U-Net architecture for medical image segmentation. In: Stoyanov, D., et al. (eds.) DLMIA/ML-CDS -2018. LNCS, vol. 11045, pp. 3–11. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00889-5_1
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This work is partially funded by Beijing Posdoctoral Research Foundation.
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Cui, H., Liu, X., Huang, N. (2019). Pulmonary Vessel Segmentation Based on Orthogonal Fused U-Net++ of Chest CT Images. In: Shen, D., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. MICCAI 2019. Lecture Notes in Computer Science(), vol 11769. Springer, Cham. https://doi.org/10.1007/978-3-030-32226-7_33
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DOI: https://doi.org/10.1007/978-3-030-32226-7_33
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