Abstract
Reconstruction and visualization of cardiac structures play significant roles in computer-aided clinical practice as well as scientific research. With the advancement of medical imaging techniques, computing facilities, and deep learning models, automatically generating whole-heart meshes directly from medical imaging data becomes feasible and shows great potential. Existing works usually employ a point cloud metric, namely the Chamfer distance, as the optimization objective when reconstructing the whole-heart meshes, which nevertheless does not take the cardiac topology into consideration. Here, we propose a novel currents-represented surface loss to optimize the reconstructed mesh topology. Due to currents’s favorable property of encoding the topology of a whole surface, our proposed pipeline delivers whole-heart reconstruction results with correct topology and comparable or even higher accuracy.
Supported by the National Natural Science Foundation of China (62071210); the Shenzhen Science and Technology Program (RCYX20210609103056042); the Shenzhen Science and Technology Innovation Committee (KCXFZ2020122117340001); the Shenzhen Basic Research Program (JCYJ20200925153847004, JCYJ20190809 120205578).
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References
Attar, R., et al.: 3D cardiac shape prediction with deep neural networks: simultaneous use of images and patient metadata. In: Shen, D., et al. (eds.) MICCAI 2019, Part II. LNCS, vol. 11765, pp. 586–594. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32245-8_65
Bucioli, A.A., et al. Holographic real time 3D heart visualization from coronary tomography for multi-place medical diagnostics. In: 2017 IEEE 15th International Conference on Dependable, Autonomic and Secure Computing, 15th International Conference on Pervasive Intelligence and Computing, 3rd International Conference on Big Data Intelligence and Computing and Cyber Science and Technology Congress (DASC/PiCom/DataCom/CyberSciTech), pp. 239–244. IEEE, 6 November 2017
Beetz, M., Banerjee, A., Grau, V.: Biventricular surface reconstruction from cine MRI contours using point completion networks. In: 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), pp. 105–109. IEEE, April 2021
Banerjee, A., Zacur, E., Choudhury, R. P., Grau, V.: Automated 3D whole-heart mesh reconstruction from 2D cine MR slices using statistical shape model. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 1702–1706. IEEE, July 2022
Charon, N., Younes, L.: Shape spaces: From geometry to biological plausibility. In: Handbook of Mathematical Models and Algorithms in Computer Vision and Imaging: Mathematical Imaging and Vision, pp. 1929–1958 (2023)
De Rham, G.: Variétés différentiables: formes, courants, formes harmoniques, vol. 3. Editions Hermann (1973)
Fischl, B.: FreeSurfer. Neuroimage 62(2), 774–781 (2012)
Garvey, C.J., Hanlon, R.: Computed tomography in clinical practice. BMJ 324(7345), 1077–1080 (2002)
González Izard, S., Sánchez Torres, R., Alonso Plaza, O., Juanes Mendez, J.A., García-Peñalvo, F.J.: NextMed: automatic imaging segmentation, 3D reconstruction, and 3D model visualization platform using augmented and virtual reality. Sensors 20(10), 2962 (2020)
Hang, S.: TetGen, a delaunay-based quality tetrahedral mesh generator. ACM Trans. Math. Softw 41(2), 11 (2015)
He, L., Ren, X., Gao, Q., Zhao, X., Yao, B., Chao, Y.: The connected-component labeling problem: a review of state-of-the-art algorithms. Pattern Recogn. 70, 25–43 (2017)
Kong, F., Shadden, S.C.: Whole heart mesh generation for image-based computational simulations by learning free-from deformations. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12904, pp. 550–559. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-87202-1_53
Kong, F., Wilson, N., Shadden, S.: A deep-learning approach for direct whole-heart mesh reconstruction. Med. Image Anal. 74, 102222 (2021)
Kong, F., Shadden, S.C.: Learning whole heart mesh generation from patient images for computational simulations. IEEE Trans. Med. Imaging 42(2), 533–545 (2022)
Lorensen, W.E., Cline, H.E.: Marching cubes: a high resolution 3d surface construction algorithm. ACM siggraph Comput. Graph. 21(4), 163–169 (1987)
Mittal, R., et al.: Computational modeling of cardiac hemodynamics: current status and future outlook. J. Comput. Phys. 305, 1065–1082 (2016)
Prakosa, A., et al.: Personalized virtual-heart technology for guiding the ablation of infarct-related ventricular tachycardia. Nat. Biomed. Eng. 2(10), 732–740 (2018)
Painchaud, N., Skandarani, Y., Judge, T., Bernard, O., Lalande, A., Jodoin, P.M.: Cardiac segmentation with strong anatomical guarantees. IEEE Trans. Med. Imaging 39(11), 3703–3713 (2020)
Pak, D.H., et al.: Distortion energy for deep learning-based volumetric finite element mesh generation for aortic valves. In: de Bruijne, M., et al. (eds.) MICCAI 2021, Part VI. LNCS, vol. 12906, pp. 485–494. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-87231-1_47
Stokes, M.B., Roberts-Thomson, R.: The role of cardiac imaging in clinical practice. Aust. Prescriber 40(4), 151 (2017)
Tang, X., Holland, D., Dale, A.M., Younes, L., Miller, M.I., Initiative, A.D.N.: Shape abnormalities of subcortical and ventricular structures in mild cognitive impairment and Alzheimer’s disease: detecting, quantifying, and predicting. Hum. Brain Mapping 35(8), 3701–3725 (2014)
Tsougos, I.: Advanced MR Neuroimaging: from Theory to Clinical Practice. CRC Press, Boca Raton (2017)
Vaillant, M., Glaunès, J.: Surface matching via currents. In: Christensen, G.E., Sonka, M. (eds.) IPMI 2005. LNCS, vol. 3565, pp. 381–392. Springer, Heidelberg (2005). https://doi.org/10.1007/11505730_32
Zhuang, X., Shen, J.: Multi-scale patch and multi-modality atlases for whole heart segmentation of MRI. Med. Image Anal. 31, 77–87 (2016)
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Yang, H., Tam, R., Tang, X. (2023). Whole-Heart Reconstruction with Explicit Topology Integrated Learning. In: Greenspan, H., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023. MICCAI 2023. Lecture Notes in Computer Science, vol 14225. Springer, Cham. https://doi.org/10.1007/978-3-031-43987-2_11
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