Abstract
Cranial implant design is aimed to repair skull defects caused by brain related diseases like brain tumor and high intracranial pressure. Researches have found that deep neural networks could potentially help accelerate the design procedure and get better results. However, most algorithms fail to handle the generalization problem: deep learning models are expected to generalize well to varied defect patterns on high-resolution skull images, while they tend to overfit to some specific defect patterns (shape, location, etc.) in the training set. We employ principle components analysis (PCA) to model the shape of healthy human skulls. We assume that defective skulls have similar shape distributions to healthy skulls in a common principle component (PC) space, as a defect, which usually only occupies a fraction of the whole skull, would not substantially deviate a human skull from its original shape distribution in a compact PC space. Applying inverse PCA to the principal components of defective skulls would therefore yield their healthy counterparts. A subtraction operation between the reconstructed healthy skulls and the defect skulls is followed to obtain the final implants. Our method is evaluated on the datasets of Task 2 and Task 3 of the AutoImplant 2021 challenge (https://autoimplant2021.grand-challenge.org/). Using only 25 healthy skulls to create the PCA model, the method nonetheless shows satisfactory results on both datasets. Results also show the good generalization performance of the proposed PCA-based method for skull shape modelling. Codes can be found at https://github.com/1eiyu/ShapePrior.
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Notes
- 1.
Not all test cases in Task 2 have a size of \(512 \times 512 \times Z\).
References
Li, J., Pepe, A., Gsaxner, C., Campe, G., Egger, J.: A baseline approach for AutoImplant: the MICCAI 2020 cranial implant design challenge. In: Syeda-Mahmood, T., et al. (eds.) CLIP/ML-CDS -2020. LNCS, vol. 12445, pp. 75–84. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-60946-7_8
Li, J., et al.: AutoImplant 2020-first MICCAI challenge on automatic cranial implant design. IEEE Trans. Med. Imaging 40(9), 2329–2342 (2021)
Matzkin, F., Newcombe, V., Glocker, B., Ferrante, E.: Cranial implant design via virtual craniectomy with shape priors. In: Li, J., Egger, J. (eds.) AutoImplant 2020. LNCS, vol. 12439, pp. 37–46. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-64327-0_5
Li, J., et al.: Automatic skull defect restoration and cranial implant generation for cranioplasty. Med. Image Anal. 73, 102171 (2021)
Ellis, D.G., Aizenberg, M.R.: Deep learning using augmentation via registration: 1st place solution to the AutoImplant 2020 challenge. In: Li, J., Egger, J. (eds.) AutoImplant 2020. LNCS, vol. 12439, pp. 47–55. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-64327-0_6
Matzkin, F., et al.: Self-supervised skull reconstruction in brain CT images with decompressive craniectomy. In: Martel, A.L. (ed.) MICCAI 2020. LNCS, vol. 12262, pp. 390–399. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-59713-9_38
Pimentel, P., et al.: Automated virtual reconstruction of large skull defects using statistical shape models and generative adversarial networks. In: Li, J., Egger, J. (eds.) AutoImplant 2020. LNCS, vol. 12439, pp. 16–27. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-64327-0_3
Avants, B.B., Tustison, N., Song, G., et al.: Advanced normalization tools (ANTS). Insight J. 2(365), 1–35 (2009)
Acknowledgement
This work was supported by the following funding agencies: CAMed (COMET K-Project 871132, see also https://www.medunigraz.at/camed/), which is funded by the Austrian Federal Ministry of Transport, Innovation and Technology (BMVIT) and the Austrian Federal Ministry for Digital and Economic Affairs (BMDW), and the Styrian Business Promotion Agency (SFG); The Austrian Science Fund (FWF) KLI 678-B31 (enFaced).
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Yu, L., Li, J., Egger, J. (2021). PCA-Skull: 3D Skull Shape Modelling Using Principal Component Analysis. In: Li, J., Egger, J. (eds) Towards the Automatization of Cranial Implant Design in Cranioplasty II. AutoImplant 2021. Lecture Notes in Computer Science(), vol 13123. Springer, Cham. https://doi.org/10.1007/978-3-030-92652-6_9
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DOI: https://doi.org/10.1007/978-3-030-92652-6_9
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