Abstract
Vessel tracing by modeling vascular structures in 3D medical images with centerlines and radii can provide useful information for vascular health. Existing algorithms have been developed but there are certain persistent problems such as incomplete or inaccurate vessel tracing, especially in complicated vascular beds like the intracranial arteries. We propose here a deep learning based open curve active contour model (DOST) to trace vessels in 3D images. Initial curves were proposed from a centerline segmentation neural network. Then data-driven machine knowledge was used to predict the stretching direction and vessel radius of the initial curve, while the active contour model (as human knowledge) maintained smoothness and intensity fitness of curves. Finally, considering the non-loop topology of most vasculatures, individually traced vessels were connected into a tree topology by applying a minimum spanning tree algorithm on a global connection graph. We evaluated DOST on a Time-of-Flight (TOF) MRA intracranial artery dataset and demonstrated its superior performance over existing segmentation-based and tracking-based vessel tracing methods. In addition, DOST showed strong adaptability on different imaging modalities (CTA, MR T1 SPACE) and vascular beds (coronary arteries).
Keywords
- Vascular tracing
- Active contour model
- Snake
- Artery modeling
- Vascular tree
- Vessel tracker
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Callaway, C.W., Carson, A.P., Chamberlain, A.M., et al.: Heart disease and stroke statistics—2020 uspdate a report from the American heart association (2020). https://doi.org/10.1161/CIR.0000000000000757
Hameeteman, K., Zuluaga, M.A., Freiman, M., et al.: Evaluation framework for carotid bifurcation lumen segmentation and stenosis grading. Med. Image Anal. 15(4), 477–488 (2011). https://doi.org/10.1016/j.media.2011.02.004
Han, K., Chen, L., Geleri, D.B., Mossa-basha, M., Hatsukami, T., Yuan, C.: Deep-learning based significant stenosis detection from multiplanar reformatted Images of traced Intracranial arteries. In: American Society of Neuroradiology 58th Annual Meeting (2020). https://doi.org/10.1002/mrm.26961
Chen, Z., Chen, L., Shirakawa, M., et al.: Intracranial vascular feature changes in time of flight MR angiography in patients undergoing carotid revascularization surgery. Magn. Reson. Imaging 75(August 2020), 45–50 (2021). https://doi.org/10.1016/j.mri.2020.10.004
Liu, W., et al.: Uncontrolled hypertension associates with subclinical cerebrovascular health globally: a multimodal imaging study. Eur. Radiol. 31(4), 2233–2241 (2020). https://doi.org/10.1007/s00330-020-07218-5
Chen, L., Sun, J., Hippe, D.S., et al.: Quantitative assessment of the intracranial vasculature in an older adult population using iCafe (intracranial artery feature extraction). Neurobiol. Aging 79, 59–65 (2019). https://doi.org/10.1016/j.neurobiolaging.2019.02.027
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). https://doi.org/10.1016/j.media.2009.07.011
Bibiloni, P., Massanet, S.: A survey on curvilinear object segmentation in multiple applications. Pattern Recognit. 60, 949–970 (2016). https://doi.org/10.1016/j.patcog.2016.07.023
Zhao, F., Chen, Y., Hou, Y., He, X.: Segmentation of blood vessels using rule-based and machine-learning-based methods: a review. Multimedia Syst. 25(2), 109–118 (2017). https://doi.org/10.1007/s00530-017-0580-7
Chen, L., Xie, Y., Sun, J., et al.: 3D intracranial artery segmentation using a convolutional autoencoder. In: 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) 3D. IEEE (2017). https://doi.org/10.1109/BIBM.2017.8217741
Wang, Y., Wei, X., Liu, F., Chen, J., Zhou, Y., Shen, W.: Deep distance transform for tubular structure segmentation in CT scans, 3833–3842 (2020)
Wang, Y., Narayanaswamy, A., Tsai, C.L., Roysam, B.: A broadly applicable 3-D neuron tracing method based on open-curve snake. Neuroinformatics 9(2–3), 193–217 (2011). https://doi.org/10.1007/s12021-011-9110-5
Wolterink, J.M., Hamersvelt, R.W., Viergever, M.A., Leiner, T., Išgum, I.: Coronary artery centerline extraction in cardiac CT angiography using a CNN-based orientation classifier. Med. Image Anal. 51, 46–60 (2019). https://doi.org/10.1016/j.media.2018.10.005
Yang, H., Chen, J., Chi, Y., Xie, X., Hua, X.: Discriminative coronary artery tracking via 3D CNN in cardiac CT angiography. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11765, pp. 468–476. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32245-8_52
Kass, M., Witkin, A., Terzopoulos, D.: Snakes: active contour models. Int. J. Comput. Vis. 1(4), 321–331 (1988). https://doi.org/10.1007/BF00133570
Wang, Y., Narayanaswamy, A., Roysam, B.: Novel 4-D open-curve active contour and curve completion approach for automated tree structure extraction. In: Proceedings of IEEE Computer and Social Conference on Computer and Vision Pattern Recognition, pp. 1105–1112 (Published online 2011). https://doi.org/10.1109/CVPR.2011.5995620
Liu, Q., Dou, Q., Heng, P.-A.: Shape-aware meta-learning for generalizing prostate MRI segmentation to unseen domains. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12262, pp. 475–485. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-59713-9_46
Chen, L., Dager, S.R., Shaw, D.W.W., et al.: A novel algorithm for refining cerebral vascular measurements in infants and adults. J. Neurosci. Methods. 340(April), 108751 (2020). https://doi.org/10.1016/j.jneumeth.2020.108751
Zhang, T.Y., Suen, C.Y.: A fast parallel algorithm for thinning digital patterns. Commun. ACM. 27(3), 236–239 (1984). https://doi.org/10.1145/357994.358023
Kruskal, J.B.: On the shortest spanning subtree of a graph and the traveling salesman problem. Proc. Am. Math. Soc. 7(1), 48 (1956). https://doi.org/10.1090/S0002-9939-1956-0078686-7
Liu, W., Chen, Z., Ortega, D., et al.: Arterial elasticity, endothelial function and intracranial vascular health: a multimodal MRI study. J. Cereb. Blood Flow Metab. 0271678X2095695 (Published online 20 October 2020). https://doi.org/10.1177/0271678X20956950
Schaap, M., Metz, C.T., van Walsum, T., et al.: Standardized evaluation methodology and reference database for evaluating coronary artery centerline extraction algorithms. Med. Image Anal. 13(5), 701–714 (2009). https://doi.org/10.1016/j.media.2009.06.003
Chen, L., Mossa-Basha, M., Balu, N., et al.: Development of a quantitative intracranial vascular features extraction tool on 3D MRA using semiautomated open-curve active contour vessel tracing. Magn. Reson. Med. 79(6), 3229–3238 (2018). https://doi.org/10.1002/mrm.26961
Chen, L., Mossa-Basha, M., Sun, J., et al.: Quantification of morphometry and intensity features of intracranial arteries from 3D TOF MRA using the intracranial artery feature extraction (iCafe): a reproducibility study. Magn Reson Imaging. 2019(57), 293–302 (2018). https://doi.org/10.1016/j.mri.2018.12.007
Bernardin, K., Stiefelhagen, R.: Evaluating multiple object tracking performance: the CLEAR MOT metrics. EURASIP J. Image Video Process. 2008(1), 1 (2008). https://doi.org/10.1155/2008/246309
Frangi, A.F., Niessen, W.J., Vincken, K.L., Viergever, M.A.: Multiscale vessel enhancement filtering. In: Wells, W.M., Colchester, A., Delp, S. (eds.) MICCAI 1998. LNCS, vol. 1496, pp. 130–137. Springer, Heidelberg (1998). https://doi.org/10.1007/BFb0056195, https://doi.org/10.1016/j.media.2004.08.001
Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28
Chen, L., Hatsukami, T., Hwang, J.-N., Yuan, C.: Automated intracranial artery labeling using a graph neural network and hierarchical refinement. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12266, pp. 76–85. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-59725-2_8
Chen, L., Sun, J., Canton, G., et al.: Automated artery localization and vessel wall segmentation using tracklet refinement and polar conversion. IEEE Access. 8, 1 (2020). https://doi.org/10.1109/access.2020.3040616
Chen, L., Geleri, D.B., Sun, J., et al.: Multi-planar, multi-contrast and multi-timepoint analysis tool (MOCHA ) for intracranial vessel wall imaging review. In: Proceedings of the Annual Meeting of the International Society for Magnetic Resonance in Medicine, 2020 (2020). https://doi.org/10.1002/mrm.24254.6
Acknowledgement
This work was supported by National Institute of Health under grant R01-NS092207. We are grateful for the collaborators who provided the datasets for this study, including the BRAVE investigators, Harborview Medical Center, and the public data from Erasmus MC, Rotterdam. We gratefully acknowledge the support of NVIDIA Corporation for donating the Titan GPU.
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Chen, L. et al. (2021). Deep Open Snake Tracker for Vessel Tracing. In: , et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2021. MICCAI 2021. Lecture Notes in Computer Science(), vol 12906. Springer, Cham. https://doi.org/10.1007/978-3-030-87231-1_56
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