Abstract
Blood vessels 3D rendering has numerous applications, ranging from diagnosis to pre-procedural and surgical approaches as it enriches vessels visualization for the clinician. In this paper, we propose a 3D blood vessels segmentation method designed for use with a cone beam computed tomography (CBCT). The algorithm constitutes a module in the development of an angio-CT visualization system, based on augmented or virtual reality as instruments supporting and improving medical decisions.
The proposed segmentation tool exploits a bone segmentation step for easing the extraction of blood vessels. Both steps are based on region growing technique. For each subject are used two CBCT acquisitions, where the first one is acquired with a traditional CT scan and is used for bone extraction, while the second one is acquired after contrast medium administration and is used for vessels reconstruction.
This novel segmentation algorithm provides an automatic and accurate tool to segment and render blood vessels tree. 3D anatomical models were viewed through a virtual reality environment in order to validate the visualization system.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bin, S., Masood, S., Jung, Y.: Virtual and augmented reality in medicine. In: Biomedical Information Technology, pp. 673–686. Elsevier (2020). https://doi.org/10.1016/B978-0-12-816034-3.00020-1
Black, J.D., Tadros, B.J.: Bone structure: from cortical to calcium. Orthop. Trauma 34(3), 113–119 (2020). https://doi.org/10.1016/j.mporth.2020.03.002
Calllara, A.L., Magliaro, C., Ahluwalia, A., Vanello, N.: Smart region-growing: a novel algorithm for the segmentation of 3D clarified confocal image stacks. Frontiers in Neuroinformatics (2018). https://doi.org/10.3389/fninf.2020.00009
Campochiaro, A.: Molecular pathogenesis of retinal and choroidal vascular diseases. Prog. Retinal Eye Res. 49, 67–81 (2015). https://doi.org/10.1016/j.preteyeres.2015.06.002
Carmeliet, P., Jain, R.K.: Angiogenesis in cancer and other diseases. Nature 407(6801), 249–257 (2000). https://doi.org/10.1038/35025220
De Momi, C., et al.: Automatic trajectory planner for stereoelectroencephalography procedures: a retrospective study vascular diseases. Trans. Biomed. Eng. 60(4), 986–996 (2013). https://doi.org/10.1109/TBME.2012.2231681
De Paolis, L.T., De Luca, V.: Augmented visualization with depth perception cues to improve the surgeon’s performance in minimally invasive surgery. Med. Biol. Eng. Comput. 57(5), 995–1013 (2019). https://doi.org/10.1007/s11517-018-1929-6
Fedorov, A., et al.: 3D slicer as an image computing platform for the quantitative imaging network. Mag. Reson. Imag. 30(9), 1323–1341 (2012). https://doi.org/10.1016/j.mri.2012.05.001
Fu, H., Xu, D., Liu, J.: Retinal vessel segmentation via deep learning network and fully-connected conditional random fields. In: IEEE 13th International Symposium on Biomedical Imaging (ISBI), vol. 16, pp. 695–701 (2016). https://doi.org/10.1109/ISBI.2016.7493362
Jones, D., Stangenberg, L., Swerdlow, N.: Image fusion and 3-Dimensional roadmapping in endovadscular surgery. Ann. Vasc. Surg. 52, 302–311 (2018). https://doi.org/10.1016/j.avsg.2018.03.032
Kadir, M.R.A., Syahrom, A., Öchsner, A.: Finite element analysis of idealised unit cell cancellous structure based on morphological indices of cancellous bone. Med. Biol. Eng. Comput. 48(5), 497–505 (2010). https://doi.org/10.1007/s11517-010-0593-2
Kikinis, R., Pieper, S.D., Vosburgh, K.G.: 3D Slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Jolesz, F.A. (ed.) Intraoperative Imaging and Image-guided Therapy, pp. 277–289. Springer, New York (2014). https://doi.org/10.1007/978-1-4614-7657-3_19
Moccia, S., De Momi, E., Sara, E.H., Mattos, L.S.: Blood vessel segmentation algorithms - review of methods, datasets and evaluation metircs. Comput. Methods Programs Biomed. 158, 71–91 (2018). https://doi.org/10.1016/j.cmpb.2018.02.001
Mühlenbruch, G., Das, M., Hohl, C.: Global left ventricular function in cardiac ct. evaluation of an automated 3D region-growing segmentation algorithm. Eur. Radiol. 16, 117–1123 (2006). https://doi.org/10.1007/s00330-005-0079-z
Rogai, F., Manfredi, C., Bocchi, L.: Metaheuristics for specialization of a segmentation algorithm for ultrasound images. IEEE Trans. Evol. Comput. 20(5), 730–741 (2016). https://doi.org/10.1109/TEVC.2016.2515660
Silva, J.N., Southworth, M., Raptis, C., Silva, J.: Emerging applications of virtual reality in cardiovascular medicine. JACC: Basic Trans. Sci. 3(3), 420–430 (2018). https://doi.org/10.1016/j.jacbts.2017.11.009
Tetteh, G., Efremov, V., Forkert, N.D.: DeepVesselNet: vessel segmentation, centerline prediction, and bifurcation detection in 3-D angiographic volumes. CoRR abs/1803.09340 (2018). https://doi.org/10.3389/fnins.2020.592352
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Simoni, A., Tiribilli, E., Lorenzetto, C., Manetti, L., Iadanza, E., Bocchi, L. (2021). 3D Vessel Segmentation in CT for Augmented and Virtual Reality. In: Hasic Telalovic, J., Kantardzic, M. (eds) Mediterranean Forum – Data Science Conference. MeFDATA 2020. Communications in Computer and Information Science, vol 1343. Springer, Cham. https://doi.org/10.1007/978-3-030-72805-2_4
Download citation
DOI: https://doi.org/10.1007/978-3-030-72805-2_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-72804-5
Online ISBN: 978-3-030-72805-2
eBook Packages: Computer ScienceComputer Science (R0)