Abstract
Designing of a cranial implant needs a 3D understanding of the complete skull shape. Thus, taking a 2D approach is sub-optimal, since a 2D model lacks a holistic 3D view of both the defective and healthy skulls. Further, loading the whole 3D skull shapes at its original image resolution is not feasible in commonly available GPUs. To mitigate these issues, we propose a fully convolutional network composed of two subnetworks. The first subnetwork is designed to complete the shape of the downsampled defective skull. The second subnetwork upsamples the reconstructed shape slice-wise. We train both the 3D and 2D networks in tandem in an end-to-end fashion, with a hierarchical loss function. Our proposed solution accurately predicts a high-resolution 3D implant in the challenge test case in terms of dice-score and the Hausdorff distance.
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
Similar content being viewed by others
References
Angelo, L., Di Stefano, P., Governi, L., Marzola, A., Volpe, Y.: A robust and automatic method for the best symmetry plane detection of craniofacial skeletons. Symmetry 11(02), 245 (2019)
Bayat, A., et al.: Inferring the 3D standing spine posture from 2D radiographs. arXiv preprint arXiv:2007.06612 (2020)
Bhowmik, A., Shit, S., Seelamantula, C.S.: Training-free, single-image super-resolution using a dynamic convolutional network. IEEE Signal Process. Lett. 25(1), 85–89 (2017)
Chen, X., Xu, L., Li, X., Egger, J.: Computer-aided implant design for the restoration of cranial defects. Sci. Rep. 7, 1–10 (2017)
Dai, A., Qi, C.R., Nießner, M.: Shape completion using 3D-encoder-predictor CNNs and shape synthesis. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 6545–6554 (2016)
Egger, J., et al.: Interactive reconstructions of cranial 3D implants under MeVisLAB as an alternative to commercial planning software. PLoS ONE 12, 20 (2017)
Ezhov, I., et al.: Real-time Bayesian personalization via a learnable brain tumor growth model. arXiv preprint arXiv:2009.04240 (2020)
Gall, M., Li, X., Chen, X., Schmalstieg, D., Egger, J.: Computer-aided planning and reconstruction of cranial 3D implants. In: Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 1179–1183, August 2016
Han, X., Li, Z., Huang, H., Kalogerakis, E., Yu, Y.: High-resolution shape completion using deep neural networks for global structure and local geometry inference. In: 2017 IEEE International Conference on Computer Vision (ICCV), pp. 85–93 (2017)
Hu, X., et al.: Feedback graph attention convolutional network for medical image enhancement. arXiv preprint arXiv:2006.13863 (2020)
Husseini, M., Sekuboyina, A., Bayat, A., Menze, B.H., Loeffler, M., Kirschke, J.S.: Conditioned variational auto-encoder for detecting osteoporotic vertebral fractures. In: Cai, Y., Wang, L., Audette, M., Zheng, G., Li, S. (eds.) CSI 2019. LNCS, vol. 11963, pp. 29–38. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-39752-4_3
Li, J., Pepe, A., Gsaxner, C., von Campe, G., Egger, J.: A baseline approach for Autoimplant: the MICCAI 2020 cranial implant design challenge. arXiv preprint arXiv:2006.12449 (2020)
Li, J., Pepe, A., Gsaxner, C., Egger, J.: An online platform for automatic skull defect restoration and cranial implant design. arXiv:2006.00980 (2020)
Marzola, A., Governi, L., Genitori, L., Mussa, F., Volpe, Y., Furferi, R.: A semi-automatic hybrid approach for defective skulls reconstruction. Comput.-Aided Des. Appl. 17, 190–204 (2019)
Morais, A., Egger, J., Alves, V.: Automated computer-aided design of cranial implants using a deep volumetric convolutional denoising autoencoder, pp. 151–160, April 2019
Navarro, F., et al.: Shape-aware complementary-task learning for multi-organ segmentation. In: Suk, H.-I., Liu, M., Yan, P., Lian, C. (eds.) MLMI 2019. LNCS, vol. 11861, pp. 620–627. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32692-0_71
Sarmad, M., Lee, H.J., Kim, Y.M.: RL-GAN-Net: a reinforcement learning agent controlled GAN network for real-time point cloud shape completion. In: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5891–5900 (2019)
Sekuboyina, A., et al.: Verse: a vertebrae labelling and segmentation benchmark. arXiv preprint arXiv:2001.09193 (2020)
Stutz, D., Geiger, A.: Learning 3D shape completion under weak supervision. Int. J. Comput. Vis. 1–20 (2018)
Sung, M., Kim, V.G., Angst, R., Guibas, L.J.: Data-driven structural priors for shape completion. ACM Trans. Graph. 34, 175:1–175:11 (2015)
Acknowledgement
Amirhossein Bayat is supported by the European Research Council (ERC) under the European Union’s ‘Horizon 2020’ research & innovation programme (GA637164–iBack–ERC–2014–STG). Suprosanna Shit is supported by the Translational Brain Imaging Training Network (TRABIT) under the European Union’s ‘Horizon 2020’ research & innovation program (Grant agreement ID: 765148).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Bayat, A., Shit, S., Kilian, A., Liechtenstein, J.T., Kirschke, J.S., Menze, B.H. (2020). Cranial Implant Prediction Using Low-Resolution 3D Shape Completion and High-Resolution 2D Refinement. In: Li, J., Egger, J. (eds) Towards the Automatization of Cranial Implant Design in Cranioplasty. AutoImplant 2020. Lecture Notes in Computer Science(), vol 12439. Springer, Cham. https://doi.org/10.1007/978-3-030-64327-0_9
Download citation
DOI: https://doi.org/10.1007/978-3-030-64327-0_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-64326-3
Online ISBN: 978-3-030-64327-0
eBook Packages: Computer ScienceComputer Science (R0)