Abstract
An accurate 3D ventricular model is essential for diagnosing and analyzing cardiovascular disease. It is challenging to obtain accurate patient-specific models on scarce data via widely accepted deep-learning methods. To fully use the characteristics of medical volume-based images, we present a slice-mask representation to better regress the parameters of the 3D model. A data synthesis strategy is proposed to alleviate the lack of training data by sampling in the constructed statistical shape model space and obtaining the corresponding slice-masks. We train the end-to-end structure by combining the segmentation and parametric regression modules. Furthermore, we establish a larger left ventricular CT dataset than before, which fills the gap in relevant data of the healthy population. Our method is evaluated on both synthetic data and real cardiac scans. Experiments demonstrate that our method can achieve advanced results in shape reconstruction and segmentation tasks. Code is publicly available at https://github.com/yuan-xiaohan/Slice-mask-based-3D-Cardiac-Shape-Reconstruction.
All the authors from Southeast University are affiliated with the Key Laboratory of Measurement and Control of Complex Systems of Engineering, Ministry of Education, Nanjing, China.
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
Adams, J., Bhalodia, R., Elhabian, S.: Uncertain-DeepSSM: from images to probabilistic shape models. In: Reuter, M., Wachinger, C., Lombaert, H., Paniagua, B., Goksel, O., Rekik, I. (eds.) ShapeMI 2020. LNCS, vol. 12474, pp. 57–72. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-61056-2_5
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. LNCS, vol. 11765, pp. 586–594. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32245-8_65
Augustin, C.M., et al.: Anatomically accurate high resolution modeling of human whole heart electromechanics: a strongly scalable algebraic multigrid solver method for nonlinear deformation. J. Comput. Phys. 305, 622–646 (2016)
Bhalodia, R., Elhabian, S.Y., Kavan, L., Whitaker, R.T.: DeepSSM: a deep learning framework for statistical shape modeling from raw images. In: Reuter, M., Wachinger, C., Lombaert, H., Paniagua, B., Lüthi, M., Egger, B. (eds.) ShapeMI 2018. LNCS, vol. 11167, pp. 244–257. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-04747-4_23
Boussaid, H., Rouet, L.: Shape feature loss for kidney segmentation in 3d ultrasound images. In: BMVC (2021)
Cai, J., Xia, Y., Yang, D., Xu, D., Yang, L., Roth, H.: End-to-End Adversarial Shape Learning for Abdomen Organ Deep Segmentation. In: Suk, H.-I., Liu, M., Yan, P., Lian, C. (eds.) MLMI 2019. LNCS, vol. 11861, pp. 124–132. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32692-0_15
Duan, J., Bello, G., Schlemper, J., Bai, W., Dawes, T.J., Biffi, C., de Marvao, A., Doumoud, G., O’Regan, D.P., Rueckert, D.: Automatic 3d bi-ventricular segmentation of cardiac images by a shape-refined multi-task deep learning approach. IEEE Trans. Med. Imaging 38(9), 2151–2164 (2019)
Ecabert, O., et al.: Automatic model-based segmentation of the heart in CT images. IEEE Trans. Med. Imaging 27(9), 1189–1201 (2008)
Fan, H., Su, H., Guibas, L.J.: A point set generation network for 3d object reconstruction from a single image. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 605–613 (2017)
Frangi, A.F., Rueckert, D., Schnabel, J.A., Niessen, W.J.: Automatic construction of multiple-object three-dimensional statistical shape models: application to cardiac modeling. IEEE Trans. Med. Imaging 21(9), 1151–1166 (2002)
Gu, Z., et al.: Ce-net: context encoder network for 2d medical image segmentation. IEEE Trans. Med. Imaging 38(10), 2281–2292 (2019)
Habijan, M., Babin, D., Galić, I., Leventić, H., Romić, K., Velicki, L., Pižurica, A.: Overview of the whole heart and heart chamber segmentation methods. Cardiovasc. Eng. Technol. 11(6), 725–747 (2020)
Hoogendoorn, C., et al.: A high-resolution atlas and statistical model of the human heart from multislice ct. IEEE Trans. Med. Imaging 32(1), 28–44 (2012)
Karim, R., et al.: Algorithms for left atrial wall segmentation and thickness-evaluation on an open-source ct and mri image database. Med. Image Anal. 50, 36–53 (2018)
Kong, F., Shadden, S.C.: Automating model generation for image-based cardiac flow simulation. J. Biomech. Eng. 142(11) (2020)
Kong, F., Shadden, S.C.: Whole heart mesh generation for image-based computational simulations by learning free-from deformations. In: de Bruijne, M., Cattin, P.C., Cotin, S., Padoy, N., Speidel, S., Zheng, Y., Essert, C. (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)
Lee, M.C.H., Petersen, K., Pawlowski, N., Glocker, B., Schaap, M.: Tetris: template transformer networks for image segmentation with shape priors. IEEE Trans. Med. Imaging 38(11), 2596–2606 (2019)
Lorensen, W.E., Cline, H.E.: Marching cubes: a high resolution 3d surface construction algorithm. ACM siggraph computer graphics 21(4), 163–169 (1987)
Mandikal, P., Navaneet, K., Agarwal, M., Babu, R.V.: 3d-lmnet: Latent embedding matching for accurate and diverse 3d point cloud reconstruction from a single image. arXiv preprint arXiv:1807.07796 (2018)
Medrano-Gracia, P., et al.: Large scale left ventricular shape atlas using automated model fitting to contours. In: Ourselin, S., Rueckert, D., Smith, N. (eds.) FIMH 2013. LNCS, vol. 7945, pp. 433–441. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-38899-6_51
Ordas, S., Oubel, E., Sebastian, R., Frangi, A.F.: Computational anatomy atlas of the heart. In: 2007 5th International Symposium on Image and Signal Processing and Analysis, pp. 338–342. IEEE (2007)
Organization, W.H.: The world health report 2002: reducing risks, promoting healthy life. World Health Organization (2002)
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
Sumner, R.W., Schmid, J., Pauly, M.: Embedded deformation for shape manipulation, pp. 80-es (2007)
Tilborghs, S., Dresselaers, T., Claus, P., Bogaert, J., Maes, F.: Shape constrained cnn for cardiac mr segmentation with simultaneous prediction of shape and pose parameters. arXiv preprint arXiv:2010.08952 (2020)
Tóthová, K., Parisot, S., Lee, M., Puyol-Antón, E., King, A., Pollefeys, M., Konukoglu, E.: Probabilistic 3D surface reconstruction from sparse MRI information. In: Martel, A.L., Abolmaesumi, P., Stoyanov, D., Mateus, D., Zuluaga, M.A., Zhou, S.K., Racoceanu, D., Joskowicz, L. (eds.) MICCAI 2020. LNCS, vol. 12261, pp. 813–823. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-59710-8_79
Wang, N., et al.: Pixel2mesh: 3d mesh model generation via image guided deformation. IEEE Trans. Pattern Anal. Mach. Intell. 43(10), 3600–3613 (2020)
Wang, Y., Zhong, Z., Hua, J.: Deeporgannet: on-the-fly reconstruction and visualization of 3d / 4d lung models from single-view projections by deep deformation network. IEEE Trans. Visual Comput. Graphics 26(1), 960–970 (2020). https://doi.org/10.1109/TVCG.2019.2934369
Wickramasinghe, U., Remelli, E., Knott, G., Fua, P.: Voxel2Mesh: 3D mesh model generation from volumetric data. In: Martel, A.L., Abolmaesumi, P., Stoyanov, D., Mateus, D., Zuluaga, M.A., Zhou, S.K., Racoceanu, D., Joskowicz, L. (eds.) MICCAI 2020. LNCS, vol. 12264, pp. 299–308. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-59719-1_30
Wolterink, J.M., et al.: An evaluation of automatic coronary artery calcium scoring methods with cardiac ct using the orcascore framework. Med. Phys. 43(5), 2361–2373 (2016)
Ye, M., Huang, Q., Yang, D., Wu, P., Yi, J., Axel, L., Metaxas, D.: PC-U Net: learning to jointly reconstruct and segment the cardiac walls in 3D from CT Data. In: Puyol Anton, E., Pop, M., Sermesant, M., Campello, V., Lalande, A., Lekadir, K., Suinesiaputra, A., Camara, O., Young, A. (eds.) STACOM 2020. LNCS, vol. 12592, pp. 117–126. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-68107-4_12
Zhou, X.-Y., Wang, Z.-Y., Li, P., Zheng, J.-Q., Yang, G.-Z.: One-stage shape instantiation from a single 2D image to 3D point cloud. In: Shen, D., Liu, T., Peters, T.M., Staib, L.H., Essert, C., Zhou, S., Yap, P.-T., Khan, A. (eds.) MICCAI 2019. LNCS, vol. 11767, pp. 30–38. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32251-9_4
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
Zhuang, X., et al.: Evaluation of algorithms for multi-modality whole heart segmentation: an open-access grand challenge. Med. Image Anal. 58, 101537 (2019)
Zotti, C., Luo, Z., Lalande, A., Jodoin, P.M.: Convolutional neural network with shape prior applied to cardiac mri segmentation. IEEE J. Biomed. Health Inform. 23(3), 1119–1128 (2018)
Acknowledgements
This work was supported in part by the Natural Science Foundation of Jiangsu Province (No. BK20220127), the National Natural Science Foundation of China (No. 62076061), the “Young Elite Scientists Sponsorship Program by CAST” (No. YES20200025), and the “Zhishan Young Scholar” Program of Southeast University (No. 2242021R41083).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
1 Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Yuan, X., Liu, C., Feng, F., Zhu, Y., Wang, Y. (2023). Slice-Mask Based 3D Cardiac Shape Reconstruction from CT Volume. In: Wang, L., Gall, J., Chin, TJ., Sato, I., Chellappa, R. (eds) Computer Vision – ACCV 2022. ACCV 2022. Lecture Notes in Computer Science, vol 13846. Springer, Cham. https://doi.org/10.1007/978-3-031-26351-4_5
Download citation
DOI: https://doi.org/10.1007/978-3-031-26351-4_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-26350-7
Online ISBN: 978-3-031-26351-4
eBook Packages: Computer ScienceComputer Science (R0)